Pre-Training Research Scientist

At Thinking Machines Lab, we are dedicated to empowering humanity through the advancement of collaborative general intelligence. Our vision is to create a future where everyone can harness the power of AI to meet their individual needs and aspirations.Our team is composed of passionate scientists, engineers, and innovators who have developed some of the most influential AI technologies, such as ChatGPT and Character.ai, as well as cutting-edge open-weight models like Mistral and acclaimed open-source projects including PyTorch, OpenAI Gym, Fairseq, and Segment Anything.About the RoleThe role of Pre-Training Researcher is pivotal to our strategic roadmap, focused on enhancing our understanding of how large models learn from data. You will investigate novel pre-training methodologies, architectures, and learning objectives aimed at making model training more efficient, robust, and aligned with human values.This position combines fundamental research with practical engineering, as we seamlessly integrate both disciplines within our team. You will be expected to produce high-performance code and engage with technical literature. This is an ideal opportunity for individuals who thrive on theoretical exploration as well as hands-on experimentation, and who aspire to influence the foundational methods by which AI learns.This is an evergreen role, meaning we keep this position open to welcome expressions of interest in this research field. We receive numerous applications, and while there may not always be an immediate fit, we encourage you to apply. We consistently review applications and will reach out as new opportunities arise. If you gain additional experience, you are welcome to reapply, but please limit your applications to once every six months. We may also post specific openings for project or team needs, where direct applications are welcome in addition to this evergreen role.What You’ll DoResearch and innovate new methodologies for pre-training.Engage in areas such as scaling, architecture, algorithms, or optimization of large-scale training runs based on your research interests and expertise.Design data curricula and sampling strategies that enhance learning dynamics and model generalization.Collaborate with infrastructure and data teams to conduct large-scale experiments in an efficient and reproducible manner.Publish and present research that propels the entire community forward, sharing code, datasets, and insights to accelerate progress across both industry and academia.

About AfterQuery AfterQuery builds training data and evaluation frameworks used by leading AI labs around the world. The team partners with advanced research groups to create high-quality datasets and run detailed evaluations that go beyond standard benchmarks. As a small, post-Series A company based in San Francisco, every team member plays a key role in shaping how future AI models learn and improve. Role Overview The Post-Training Research Scientist focuses on proving the impact of AfterQuery's datasets. This work involves designing and running training experiments to isolate how specific data influences model performance. Projects span Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) post-training, with an emphasis on measuring effects on capability, generalization, and alignment. Working closely with partner labs, the scientist turns data into clear, verifiable results: showing exactly how a dataset leads to measurable improvements under defined conditions. The work is experimental and directly shapes the value of AfterQuery's products. What You Will Do Run controlled SFT and RL experiments to measure how datasets affect model outcomes. Quantify gains in areas like reasoning, tool use, long-horizon tasks, and specialized workflows. Share findings with partner labs to support sales and demonstrate value. Work with internal subject matter experts to improve data quality based on experimental results. What We Look For Strong background in LLM training and evaluation methods. Curiosity about how data structure, selection, and quality shape model behavior. Skill in designing experiments, executing quickly, and drawing practical insights from complex results. Comfort working across fields such as finance, software engineering, and policy. Focus on real-world implementation, not just theory. Research experience at the undergraduate or master's level is preferred; a PhD is not required. Compensation $250,000 - $450,000 total compensation plus equity

About the RoleIn the realm of machine learning, pretraining lays the foundation for a general model, while post-training refines that model, enhancing its utility, controllability, safety, and performance in real-world applications. As a Post-Training Research Scientist, you will transform large pretrained robot models into production-ready systems through methodologies such as fine-tuning, reinforcement learning, steering, human feedback, task specialization, evaluation, and on-robot validation at scale. This position offers a unique opportunity for individuals from diverse backgrounds to evolve into full-stack ML roboticists, adept at swiftly identifying challenges across machine learning and control domains. This is where innovative research converges with practical implementation.Your Responsibilities Include:Crafting fine-tuning and adaptation strategies tailored for specific robotic tasks and embodiments.Developing methodologies to enhance reliability, robustness, and controllability of robotic systems.Establishing evaluation frameworks to assess real-world robot performance beyond just offline metrics.Collaborating with ML infrastructure teams to optimize inference-time performance, including latency, stability, and memory usage.Utilizing advanced techniques such as imitation learning, reinforcement learning, distillation, synthetic data, and curriculum learning.Bridging the gap between model outputs and tangible outcomes in the physical world.You Might Excel in This Role If You:Possess experience in fine-tuning large models for downstream applications, including RLHF, imitation learning, reinforcement learning, distillation, and domain adaptation.Have a background in embodied AI, robotics, or real-world machine learning systems.Demonstrate a strong commitment to evaluation, benchmarking, and failure analysis.Are comfortable troubleshooting and debugging across the entire ML stack, from analyzing loss curves to understanding robot behavior.Enjoy rapid iteration and thrive on real-world feedback loops.Aspire to connect foundational models with practical deployment scenarios.About GeneralistAt Generalist, we are dedicated to realizing the vision of general-purpose robots. We envision a future where industries and homes benefit from collaborative interactions between humans and machines, enabling us to achieve more than ever before. Our focus is on building embodied foundation models, starting with dexterity, and advancing the frontiers of data, models, and hardware to empower robots to intelligently engage with their environments.

Zyphra is an innovative leader in artificial intelligence, located in the heart of San Francisco, California.Role Overview:As a Research Engineer specializing in Language Model Pre-Training, you will play a pivotal role in defining our language model strategy through comprehensive pretraining development. Your close collaboration with our pretraining team will ensure that your insights contribute to the advancement of our next-generation models.Key Responsibilities:Conduct large-scale training runs and implement model parallelization techniques.Optimize the performance of our pretraining stack.Oversee dataset collection, processing, and evaluation.Research architecture and methodologies, including optimizer ablations.Qualifications:Demonstrated engineering prowess in developing reliable and robust systems.A quick learner with a passion for implementing innovative ideas.Exceptional communication and collaboration skills, capable of working effectively on both research and engineering implementations at scale.Preferred Skills:Profound expertise in addressing machine learning challenges and training models.Experience training on large-scale (multi-node) GPU clusters.In-depth understanding of model training pipelines, including model/data parallelism and distributed optimizers.Strong methodology for conducting rigorous ablations and hypothesis testing.Familiarity with large-scale, high-performance data processing pipelines.High proficiency in PyTorch and Python programming.Ability to navigate and understand extensive pre-existing codebases swiftly.Published research in machine learning in reputable venues is an advantage.Postgraduate degree in a relevant scientific field (Computer Science, Electrical Engineering, Mathematics, Physics).Why Join Zyphra?We value a research methodology that emphasizes thoughtful, methodical progress towards ambitious objectives. Both deep research and engineering excellence are given equal importance.Join us in an environment that fosters innovation, collaboration, and professional growth.

Join Baseten as a Post-Training Research Scientist, where you will play a vital role in advancing our machine learning capabilities. In this position, you will have the opportunity to conduct innovative research, analyze data, and contribute to the development of cutting-edge technologies. Your work will directly impact our projects and enhance the performance of our models.

About the TeamJoin the innovative Post-Training team at OpenAI, where we focus on refining and elevating pre-trained models for deployment in ChatGPT, our API, and future products. Collaborating closely with various research and product teams, we conduct crucial research that prepares our models for real-world deployment to millions of users, ensuring they are safe, efficient, and reliable.About the RoleAs a Research Engineer / Scientist, you will spearhead the research and development of enhancements to our models. Our work intersects reinforcement learning and product development, aiming to create cutting-edge solutions.We seek passionate individuals with robust machine learning engineering skills and research experience, particularly with innovative and powerful models. The ideal candidate will be driven by a commitment to product-oriented research.This position is located in San Francisco, CA, and follows a hybrid work model requiring three days in the office each week. Relocation assistance is available for new employees.In this role, you will:Lead and execute a research agenda aimed at enhancing model capabilities and performance.Work collaboratively with research and product teams to empower customers to optimize their models.Develop robust evaluation frameworks to monitor and assess modeling advancements.Design, implement, test, and debug code across our research stack.You may excel in this role if you:Possess a deep understanding of machine learning and its applications.Have experience with relevant models and methodologies for evaluating model improvements.Are adept at navigating large ML codebases for debugging purposes.Thrive in a fast-paced and technically intricate environment.About OpenAIOpenAI is a pioneering AI research and deployment organization dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We are committed to pushing the boundaries of AI capabilities while prioritizing safety and human-centric values in our products. Our mission is to embrace diverse perspectives, voices, and experiences that represent the full spectrum of humanity, as we strive for a future where AI is a powerful ally for everyone.

At Scale AI, we collaborate with leading AI laboratories to supply high-quality data and foster advancements in Generative AI research. We seek innovative Research Scientists and Research Engineers with a strong focus on post-training techniques for Large Language Models (LLMs), including Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), and reward modeling. This position emphasizes optimizing data curation and evaluation processes to boost LLM performance across text and multimodal formats. In this pivotal role, you will pioneer new methods to enhance the alignment and generalization of extensive generative models. You will work closely with fellow researchers and engineers to establish best practices in data-driven AI development. Additionally, you will collaborate with top foundation model labs, providing critical technical and strategic insights for the evolution of next-generation generative AI models.

Advancing Self-Improving SuperintelligenceAt Letta, we are on a mission to revolutionize artificial intelligence by creating self-improving agents that learn and adapt like humans. Unlike current AI systems that are often rigid and brittle, our innovative approach aims to build adaptable AI that continually evolves through experience.Founded by the visionaries behind MemGPT at UC Berkeley's Sky Computing Lab, the birthplace of Spark and Ray, we are backed by notable figures in AI infrastructure, including Jeff Dean and Clem Delangue. Our agents are already enhancing production systems for industry leaders such as 11x and Bilt Rewards, continually learning and improving in real-time.Join our elite team of researchers and engineers dedicated to tackling AI's most significant challenges: creating machines that can reason, remember, and learn as humans do.This position requires in-person attendance (no hybrid options) at our downtown San Francisco office, five days a week.

Your Contribution at LilaAs a Machine Learning Research Scientist I/II specializing in LLM Inference, you will spearhead research initiatives focused on the training and deployment of large language models for scientific applications.Your ResponsibilitiesDevelop and refine post-training strategies for LLMs, including Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), and Reinforcement Learning with verifiers.Design efficient inference mechanisms and compute strategies for complex tool utilization in various environments.Create scalable evaluation metrics to assess LLM performance in scientific reasoning tasks.Investigate the boundaries of cutting-edge LLM methodologies for scientific challenges and analyze their limitations.

Genmo is a pioneering research laboratory dedicated to advancing cutting-edge models for video generation, with the mission of unlocking the creative potential of Artificial General Intelligence (AGI). We invite you to be a part of our innovative team, where you can contribute to shaping the future of AI and expanding the horizons of video generation technology.Role Overview:We are on the lookout for a talented Research Scientist to join our dynamic team, specializing in alignment and post-training methodologies for large-scale video generation models. In this pivotal role, you will be instrumental in ensuring our diffusion-based video models consistently deliver high-quality, physically accurate, and safe outputs that align with human values and preferences.Key Responsibilities:Lead groundbreaking research initiatives in alignment and post-training strategies for video generation models, prioritizing enhanced quality, reliability, and alignment with human intent.Design and implement supervised fine-tuning and reinforcement learning from human feedback (RLHF) pipelines for video generation models.Establish robust evaluation frameworks to assess model alignment, safety, and output quality.Create and optimize data collection pipelines for capturing human feedback and preferences.Conduct experiments to validate alignment techniques and their scalability.Collaborate with cross-functional teams to incorporate alignment enhancements into our production workflow.Stay abreast of the latest developments by reviewing academic literature in generative AI and alignment.Mentor junior researchers and promote a culture of responsible AI development.Partner closely with product teams to ensure that alignment methods enhance model capabilities.Qualifications:Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a closely related field.Demonstrated excellence with a strong publication record in top-tier conferences (e.g., NeurIPS, ICML, ICLR) focusing on reinforcement learning, alignment, or generative models.Extensive experience in implementing and optimizing large-scale training pipelines utilizing PyTorch.In-depth understanding of reinforcement learning techniques, especially RLHF.Proficient in distributed training systems and conducting large-scale experiments.Proven ability to design and implement robust evaluation strategies for models.

Join OpenAI as a Research Scientist and explore cutting-edge machine learning innovations. In this role, you will be at the forefront of developing groundbreaking techniques while advancing our team's research initiatives. Collaborate with talented peers across various teams to discover transformative ideas that scale effectively. We seek individuals who are passionate about pushing the boundaries of AI and want to contribute to our unified research vision.

Merge Labs is an innovative research facility dedicated to merging biological sciences and artificial intelligence to enhance human capability, autonomy, and experience. Our mission is to pioneer revolutionary methodologies in brain-computer interfaces that facilitate high-bandwidth interactions with the brain, seamlessly integrate advanced AI, and maintain safety and accessibility for all users.About the TeamAt Merge, we are addressing some of the most ambitious challenges in molecular engineering, synthetic biology, and neuroscience. Our Research Platform Team is responsible for creating the experimental frameworks necessary to tackle these challenges with exceptional speed and precision. The tools and methodologies developed by our team significantly enhance molecular assembly, protein expression, mammalian cell culture, advanced microscopy, sequencing, and unique custom techniques. We collaborate with program teams to establish and optimize these capabilities, implement automation where beneficial, and integrate with our data science and machine learning pipelines, continuously pushing the boundaries of throughput and innovation.About the RoleAs a Platform Scientist, you will be instrumental in developing high-efficiency and high-throughput experimental pipelines that accelerate research initiatives. You will work closely with program leads, project scientists, data scientists, and engineers, leading your work and potentially recruiting additional team members as necessary.Key Responsibilities:Collaborate with program leads and scientists to identify critical experimental requirements and workflows.Develop processes to facilitate high-throughput and/or high-efficiency experiments, including reagent production and analysis.Scope, procure, construct, program, and validate instruments to support experimental workflows.Ensure the quality, reliability, and integrity of data generated from automated pipelines, including defining and implementing suitable quality control checkpoints.Work alongside data science and machine learning engineers to incorporate metadata tracking, computational design, and analysis into experimental pipelines.Partner with electrical, mechanical, and software engineers to create custom setups.Innovate and validate concepts to enhance experimental throughput.

OverviewBecome an integral part of our dynamic R&D team dedicated to developing fully automated research systems that push the boundaries of AI. Zochi has achieved a milestone by publishing the first entirely AI-generated A* conference paper. Locus has set a new industry standard as the first AI system to surpass human experts in AI R&D.Key ResponsibilitiesConceptualize and develop innovative architectures for automated research.Work collaboratively within a specialized team of researchers addressing cutting-edge challenges in long-horizon agentic capabilities, post-training for open-ended objectives, and environment crafting.Document and publish key internal findings alongside success stories from external collaborations.QualificationsPhD or equivalent research experience in Computer Science, Machine Learning, Artificial Intelligence, or a related discipline. Outstanding candidates with significant research contributions are encouraged to apply, regardless of formal qualifications.Demonstrated history of impactful AI/ML research contributions in academic or corporate environments.Expertise in developing long-horizon, multi-agent systems and/or model post-training, especially in scientific domains or for open-ended discovery objectives.A strong passion for advancing problem-solving processes and scientific discovery, thriving in high-autonomy roles and environments.Our CultureCompetitive compensation and equity options.Unlimited Paid Time Off (PTO), emphasizing team collaboration and a community-focused workplace.Opportunities for conference participation and engagement in community initiatives.Empowered roles with high levels of responsibility.#1: We are a small, passionate team of leading investors, researchers, and industry experts committed to the mission of accelerating discovery. Join us.

About Our TeamJoin the forefront of AI innovation with the RL and Reasoning team at OpenAI. Our team is dedicated to advancing reinforcement learning research and has pioneered transformative projects, including o1 and o3. We are committed to pushing the limits of generative models while ensuring their scalable deployment.About the RoleAs a Research Engineer/Research Scientist at OpenAI, you will play a pivotal role in enhancing AI alignment and capabilities through state-of-the-art reinforcement learning techniques. Your contributions will be essential in training intelligent, aligned, and versatile agents that power various AI models.We seek individuals with a solid foundation in reinforcement learning research, agile coding skills, and a passion for rapid iteration.This position is located in San Francisco, CA, and follows a hybrid work model of three days in the office per week. We also provide relocation assistance for new hires.You may excel in this role if:You are enthusiastic about being at the cutting edge of RL and language model research.You take initiative, owning ideas and driving them to fruition.You value principled methodologies, conducting simple experiments in controlled environments to draw trustworthy conclusions.You thrive in a fast-paced, complex technical environment where rapid iteration is essential.You are adept at navigating extensive ML codebases to troubleshoot and enhance them.You possess a profound understanding of machine learning and its applications.About OpenAIOpenAI is a pioneering AI research and deployment organization committed to ensuring that general-purpose artificial intelligence serves the greater good for humanity. We strive to push the boundaries of AI system capabilities while prioritizing safe deployment through our innovative products. We recognize AI as a powerful tool that must be developed with safety and human-centric principles, embracing diverse perspectives to reflect the full spectrum of humanity.We are proud to be an equal opportunity employer, welcoming applicants from all backgrounds without discrimination based on race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or any other legally protected characteristic.

Join Our Innovative TeamAt OpenAI, our Training team is at the forefront of developing advanced language models that drive our research and products, getting us closer to achieving Artificial General Intelligence (AGI). This mission demands a blend of cutting-edge research to enhance our architecture, datasets, and optimization methods, alongside strategic long-term initiatives that boost the efficiency and capabilities of future models. We ensure that our models, including recent breakthroughs like GPT-4-Turbo and GPT-4o, adhere to the highest standards of excellence.Your RoleAs an integral member of our architecture team, you will spearhead architectural advancements for OpenAI’s leading models, enhancing their intelligence and efficiency while introducing novel capabilities. Your expertise in large language model (LLM) architectures and model inference will be crucial as you adopt a hands-on, empirical approach to problem-solving. Whether brainstorming creative breakthroughs, refining foundational systems, designing evaluations, or diagnosing performance issues, your diverse skill set will be invaluable.This position is located in San Francisco, where we embrace a hybrid work environment of three days in the office each week, and we provide relocation support for new hires.Your Key Responsibilities:Innovate, prototype, and upscale new architectures to elevate model intelligence.Conduct and evaluate experiments both independently and collaboratively.Analyze, debug, and enhance both model performance and computational efficiency.Contribute to the development of training and inference infrastructure.Who You Are:You possess experience with significant contributions to major LLM training projects.You excel at independently evaluating and enhancing deep learning architectures.You are driven to responsibly implement LLMs in real-world applications.You are knowledgeable about state-of-the-art transformer modifications aimed at improving efficiency.About OpenAIOpenAI is a pioneering AI research and deployment organization committed to ensuring that artificial general intelligence benefits humanity. We focus on developing safe and effective AI technologies that empower individuals and organizations across the globe.

Join Our Team as a Research ScientistAt Parallel, we are at the forefront of web infrastructure innovation, enabling businesses across sectors such as sales, marketing, insurance, and technology to harness the power of AI. Our state-of-the-art products empower users to develop superior AI agents with seamless and flexible access to the web.With significant backing of $130 million from prominent investors like Kleiner Perkins, Index Ventures, and Spark Capital, we are dedicated to redefining the web for artificial intelligence. As we expand, we're assembling a top-tier team of engineers, designers, marketers, sales experts, researchers, and operational specialists committed to our vision.Your Role: As a Research Scientist, you will tackle the challenge of training and scaling models designed to enhance web indexing capabilities.About You: You possess a profound understanding of contemporary models and training methodologies. You enjoy engaging in discussions about the convergence of search, recommendations, and transformer models, and are passionate about translating your research into impactful products and systems utilized by millions.

Zyphra is a pioneering artificial intelligence firm located in the vibrant city of San Francisco, California.About the Role:We are seeking a passionate Research Scientist to join our dynamic Agency and Reasoning Team at Zyphra. In this role, you will conduct cutting-edge research in reinforcement learning, post-training methodologies, and human preference learning. Your innovative ideas will be instrumental in shaping our next-generation language models, enabling their application on a large scale.What We Desire:A strong sense of research intuition and tasteCapability to navigate a research project from initial concept to execution and documentationProficiency in implementation and prototypingA quick thinker who can rapidly transform ideas into experimental frameworksAbility to collaborate effectively in a fast-paced research environmentAn insatiable curiosity and enthusiasm for the study of intelligence.Qualifications:Proven experience and skill in reinforcement learning, particularly in the context of language model reasoning or classical RL tasksFamiliarity with language-model-supervised fine-tuning and preference-learning techniques, such as DPO and simPO.Experience with methods for context-length extensionStrong intuitive understanding of model behaviors, with the ability to refine them through iterative fine-tuningInterest in engaging deeply with data and dedicating time to data engineering and synthetic data generationA postgraduate degree in a scientific discipline (Computer Science, Electrical Engineering, Mathematics, Physics)Published research in reputable machine learning venuesExpertise in PyTorch and PythonEagerness and aptitude for rapidly acquiring new knowledge and implementing innovative conceptsExceptional communication and teamwork abilities, capable of contributing to both research and large-scale engineering effortsWhy Join Zyphra?We champion creative and unconventional ideas and are prepared to invest significantly in innovative concepts.Our culture fosters collaboration, curiosity, and intellectual growth.

Zyphra is a cutting-edge artificial intelligence firm headquartered in the vibrant city of San Francisco, California.Position Overview:As a Research Scientist specializing in Model Architectures, you will play a pivotal role in Zyphra’s AI Architecture Research Team. Your responsibilities will include the design and thorough evaluation of innovative model architectures and training methodologies aimed at enhancing essential modeling capabilities (e.g., loss per flop or loss per parameter) and tackling core limitations inherent in current models. You will collaborate closely with our pre-training team to ensure that your findings are seamlessly integrated into our next-generation models.Qualifications:A strong research acumen and intuition.Proven ability to navigate research projects from initial conception to execution and final write-up.Exceptional implementation and prototyping skills, with the capability to swiftly transform ideas into experimental outcomes.A collaborative spirit and the ability to thrive in a fast-paced research environment.A deep curiosity and enthusiasm for understanding intelligence.Requirements:Experience with long-term memory, RAG/retrieval systems, dynamic/adaptive computation, and alternative credit assignment strategies.Knowledge of reinforcement learning, control theory, and signal processing techniques.A passion for exploring and critically evaluating unconventional ideas, with the ability to maintain a unique perspective.Familiarity with modern training pipelines and the hardware necessities for designing efficient architectures compatible with GPU hardware.Strong understanding of experimental methodologies for conducting rigorous ablations and hypothesis testing.High proficiency in PyTorch and Python programming.Ability to quickly assimilate into large pre-existing codebases and contribute effectively.Prior publication of machine learning research in reputable venues.Postgraduate degree in a scientific discipline (e.g., Computer Science, Electrical Engineering, Mathematics, Physics).Why Join Zyphra?We emphasize a structured research methodology that systematically addresses ambitious challenges in AI.

Our MissionAt Reflection AI, our goal is to develop open superintelligence and make it universally accessible.We are pioneering open weight models tailored for individuals, agents, enterprises, and even entire nations. Our diverse team comprises talented AI researchers and industry veterans from prestigious organizations such as DeepMind, OpenAI, Google Brain, Meta, Character.AI, Anthropic, and many more.Role OverviewConstruct and enhance distributed training systems that drive the pre-training of cutting-edge models.Collaborate with research teams to design and execute extensive training runs for foundational models.Create infrastructure that facilitates efficient training across thousands of GPUs leveraging contemporary distributed training frameworks.Enhance training throughput, stability, and efficiency for extensive model training tasks.Work closely with pre-training researchers to convert experimental concepts into scalable, production-ready training systems.Boost performance of distributed training tasks through optimization of communication, memory management, and GPU utilization.Develop and maintain training pipelines that accommodate large-scale datasets, checkpointing, and iterative experiments.Identify and resolve performance bottlenecks within distributed training systems, including model parallelism, GPU communication, and training runtime environments.Contribute to the creation of systems that promote swift experimentation and iteration on novel training methods.

About AfterQuery AfterQuery partners with leading AI labs to advance training data and evaluation frameworks. The team builds high-signal datasets and runs thorough evaluations that go beyond standard benchmarks. As a post-Series A, early-stage company in San Francisco, AfterQuery gives each team member room to shape the future of AI models. Role Overview: Research Scientist - Frontier Data This role focuses on designing datasets and developing evaluation systems that influence how top AI models are trained and assessed. Working closely with research teams at major AI labs, the scientist explores new data collection techniques, investigates where models fall short, and sets up metrics to track progress. The work is hands-on and experimental, moving quickly from hypothesis to live testing and directly impacting large-scale model training. Key Responsibilities Design data slides and analyze data structures to uncover model weaknesses in areas like finance, software development, and enterprise operations. Build and refine evaluation rubrics and reward signals for RLHF and RLVR training approaches. Study annotator behavior and run experiments to improve model capabilities across different domains. Develop quantitative frameworks to measure dataset quality, diversity, and their effect on model alignment and performance. Work with research teams to turn training objectives into concrete data and evaluation needs. What We Look For Experience as an undergraduate or master’s research student (PhD not required). Background or internships with RL environments or AI safety and benchmarking organizations (e.g., METR, Artificial Analysis) is a strong plus. Genuine interest in how data structure, selection, and quality affect model outcomes. Demonstrated skill in designing experiments, acting quickly, and extracting insights from complex data. Comfort working across sectors such as finance, software engineering, and policy. Strong quantitative background and familiarity with LLM training pipelines, RLHF/RLVR methods, or evaluation frameworks. A hands-on mindset focused on building practical solutions.