Research Engineer / Research Scientist, Post-Training

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.

At Genmo, we are pioneering advancements in video generation technology through our state-of-the-art research lab. Our mission is to develop open models that contribute to the evolution of Artificial General Intelligence (AGI). Join us as we redefine the capabilities of AI and explore the vast potential of video generation.Role Overview:We are on the lookout for an outstanding Research Scientist specializing in diffusion models to be a part of our innovative team. Your primary focus will be on creating advanced diffusion models aimed at transforming text into captivating video content. This role places you at the cutting edge of AI research, where you will devise new architectures and algorithms to generate visually appealing and coherent videos from textual descriptions.

At Genmo, we are pioneers in developing cutting-edge models for video generation, aiming to unlock the full potential of Artificial General Intelligence (AGI). Join our innovative team and play a vital role in redefining the landscape of AI technology.About the RoleWe are on the lookout for a talented Research Engineer to enhance our research team dedicated to advancing visual generative AI. In this role, you will collaborate with experienced professionals to refine and deploy generative models safely and effectively.What You'll DoDevelop novel machine learning techniques tailored for visual generative modeling.Experiment with diverse generative architectures including Diffusion Models, GANs, and Transformers.Optimize model performance and scalability.Ensure safe and robust deployment of models.Work collaboratively with cross-functional teams to integrate research advancements.Engage in research discussions and contribute to technical documentation.Required QualificationsBachelor's or Master's degree in Computer Science, Machine Learning, or a related field (recent graduates encouraged to apply).A minimum of 2 years’ experience in Machine Learning.Strong understanding of machine learning principles, particularly in generative models.Proficient programming skills in Python, along with frameworks like PyTorch or TensorFlow.Experience with deep learning frameworks and model training techniques.Familiarity with core concepts in computer vision and generative AI.Hands-on project experience (academic or personal) with generative models.Excellent communication skills and a collaborative spirit to thrive in a research team environment.Preferred QualificationsResearch projects or thesis work in the field of generative AI.Experience with various generative models such as:Diffusion ModelsGANsTransformer architecturesLarge Language Models

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.

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.

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.

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

Join Cartesia: Pioneering AI InnovationAt Cartesia, we are on a mission to redefine the landscape of artificial intelligence. Our goal is to create the next generation of AI that is interactive, ubiquitous, and capable of continuous reasoning across vast streams of audio, video, and text data. With an impressive foundation built on our pioneering work in State Space Models (SSMs) at the Stanford AI Lab, our team is uniquely positioned to advance model architectures that will make on-device reasoning a reality.Backed by prominent investors like Index Ventures and Lightspeed Venture Partners, along with a network of 90+ advisors, including top experts in AI, we are committed to pushing the boundaries of model innovation and systems engineering.About the RoleWe believe that the next significant advancement in model intelligence will stem from enhanced post-training methods and alignment strategies. As a Post-Training Researcher, you will be at the forefront of developing systems and methodologies that ensure our multimodal models are not just adaptive, but also aligned with human intentions.In this role, you will collaborate across machine learning research, alignment, and infrastructure, crafting innovative techniques for preference optimization, model evaluation, and feedback-driven learning. You will investigate how feedback signals can enhance reasoning capabilities across various modalities while establishing the necessary infrastructure to scale and improve these processes.Your contributions will be pivotal in shaping the learning and improvement trajectory of Cartesia’s foundational models, ultimately enhancing their connection with users.Your ImpactLead research initiatives aimed at enhancing the capabilities and alignment of multimodal models.Create cutting-edge post-training methods and evaluation frameworks to assess model advancements.Collaborate closely with research, product, and platform teams to establish best practices for specialized model development.Design, debug, and scale experimental systems to ensure reliability and reproducibility throughout training cycles.Convert research insights into production-ready systems that enhance model reasoning, consistency, and alignment with human values.

Role overview OpenAI is looking for a Researcher focused on Agentic Post-Training, based in San Francisco. This role centers on analyzing and improving how AI systems behave after their initial training. The goal is to broaden the capabilities of AI and refine how models respond in complex situations. What you will do Study and assess agentic behaviors in trained AI models Create new approaches to strengthen these behaviors after training Collaborate with a talented team on projects that shape the future of artificial intelligence research Collaboration and impact This position involves hands-on research with other specialists at OpenAI. The work directly supports the advancement of AI capabilities and helps define new benchmarks for agentic performance in artificial intelligence.

At Thinking Machines Lab, our mission is to empower humanity by advancing collaborative general intelligence. We envision a future where everyone can harness the knowledge and tools necessary for AI to serve their unique needs and aspirations. Our team comprises scientists, engineers, and builders who have developed some of the most widely utilized AI products, such as ChatGPT and Character.ai, as well as open-weight models like Mistral and popular open-source projects including PyTorch, OpenAI Gym, Fairseq, and Segment Anything.About the RoleThe role of a Post-Training Researcher is pivotal to our strategic vision. This position serves as the essential link between raw model intelligence and a practical, safe, and collaborative system for human users.Our research in post-training data sits at the intersection of human insights and machine learning. By integrating human and synthetic data techniques alongside innovative methodologies, we capture the subtleties of human behavior to inform and guide our models. We investigate and model the mechanisms that derive value for individuals, enabling us to articulate, predict, and enhance human preferences, behaviors, and satisfaction. Our objective is to translate research concepts into actionable data through meticulously planned data labeling and collection initiatives, while also understanding the science behind high-quality data that effectively trains our models. Additionally, we develop and assess quantitative metrics to evaluate the success and impact of our data and training strategies.Beyond execution, we explore new paradigms for human-AI interaction and scalable oversight, experimenting with optimal ways for humans to supervise, guide, and collaborate with models. This interdisciplinary role merges research, data operations, and technical implementation, pushing the boundaries of aligned, human-centered AI systems.This position combines foundational research and practical engineering, as we do not differentiate between these roles internally. You will be expected to write high-performance code and comprehend technical reports. This role is perfect for individuals who thrive on deep theoretical exploration and hands-on experimentation, eager to shape the foundational aspects of AI learning.Note: This is an evergreen role that we maintain continuously to express interest in this research area. We receive a high volume of applications, and while there may not always be an immediate fit for your skills and experience, we encourage you to apply. We regularly review applications and reach out to candidates as new opportunities arise. You are welcome to reapply after gaining more experience, but please limit applications to once every six months. You may also notice postings for specific roles for targeted positions.

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.

Join Baseten as a Post-Training Research Engineer and contribute to groundbreaking advancements in machine learning and AI. In this role, you will leverage your engineering skills to analyze and enhance models post-training, ensuring optimal performance and efficiency.

Join the Center for AI Safety (CAIS), a pioneering research and advocacy organization dedicated to addressing the societal-scale risks posed by artificial intelligence. We tackle the most pressing challenges in AI through rigorous technical research, innovative field-building initiatives, and proactive policy engagement, in collaboration with our sister organization, the Center for AI Safety Action Fund.As a Research Scientist, you will spearhead and conduct transformative research aimed at enhancing the safety and dependability of cutting-edge AI systems. Your responsibilities will include designing and executing experiments on large language models, developing the necessary tools for training and evaluating models at scale, and converting your findings into publishable research. You will work closely with CAIS researchers and external partners from academia and industry, utilizing our compute cluster for large-scale model training and evaluation. Your research will focus on critical areas such as AI honesty, robustness, transparency, and the detection of trojan/backdoor behaviors, all aimed at mitigating real-world risks associated with advanced AI technologies.

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.

RoleAs a Research Scientist at OpenEvidence, you will explore the potential of cutting-edge models, leveraging a strong research foundation to build practical systems rather than merely publishing papers. You will take ownership of projects from conception to execution, valuing evaluation and quantitative validation as integral components of your work.We seek exceptional innovators who are not confined to narrow specializations. Our engineers and scientists collaborate across diverse products and projects, driving impactful work wherever their skills can shine.About UsOpenEvidence is the leading medical AI platform globally, rapidly adopted by over 40% of US clinicians within just one year through organic, product-driven growth. With a valuation of $12B, our engineering team comprises 30 talented individuals from prestigious institutions like MIT, Harvard, and Stanford. We believe that transformative products emerge from a select group of exceptional builders who are empowered to take initiative and work swiftly toward focused goals. Join us as we embark on a unique opportunity to establish the standard platform for medical AI.CultureWe hold that work should engage at a world-class level. Building from 0 to 1 and scaling from 1 to 1000 is akin to a professional sport, where uncompromising excellence is the standard. We assert that the creation of unprecedented technologies requires complete ownership, and significant achievements arise when individuals commit to them.Who Are You?If your goal is to clock in and out with minimal engagement, this role is not for you. If you prefer to write papers rather than roll up your sleeves and get involved in creating impactful solutions that influence millions, this position may be your calling. The ideal candidate is a remarkable builder—intelligent, ambitious, resourceful, self-driven, meticulous, motivated, diligent, and humble. We recognize this profile is rare, which is why we have only identified 30 such individuals and are eager to find more.LocationAll engineering roles are in-person, requiring attendance five days a week in either San Francisco or Miami.

Join the Center for AI Safety (CAIS), a premier research and advocacy organization dedicated to addressing the complex societal challenges posed by artificial intelligence (AI). Our mission focuses on mitigating large-scale risks associated with AI through groundbreaking technical research, strategic initiatives, and proactive policy engagement, in collaboration with our sister organization, the Center for AI Safety Action Fund. As a Senior Research Scientist at CAIS, you will spearhead and execute transformative research aimed at enhancing the safety and reliability of advanced AI systems. You will take ownership of significant open challenges, driving them to successful publication. We seek individuals who set a high standard for research excellence and contribute innovative ideas to elevate our collective understanding. Your role will involve designing and conducting experiments on large language models, developing the necessary tools for large-scale model training and evaluation, and translating findings into publishable research. Close collaboration with CAIS researchers and external academic and industry partners will be essential, utilizing our compute cluster for extensive training and evaluation projects. Research areas include AI honesty, robustness, transparency, and mitigating trojan/backdoor behaviors, all geared towards reducing real-world risks from sophisticated AI systems.

At Thinking Machines Lab, our mission is to empower humanity by advancing collaborative general intelligence. We strive to build a future where everyone has access to the knowledge and tools essential for making AI work effectively for their unique objectives.Our team comprises scientists, engineers, and innovators who have contributed to some of the most widely adopted AI products, including ChatGPT and Character.ai, as well as notable open-weight models like Mistral and popular open-source projects such as PyTorch, OpenAI Gym, Fairseq, and Segment Anything.About the RoleThe Post-Training Researcher position is pivotal to our roadmap. It serves as a crucial connection between raw model intelligence and a system that is genuinely beneficial, safe, and collaborative for human users.This role uniquely combines fundamental research with practical engineering, as we do not differentiate between these functions internally. Candidates will be expected to produce high-performance code and analyze technical reports. This position is ideal for individuals who relish both deep theoretical inquiry and hands-on experimentation, aiming to influence the foundational aspects of AI learning.Note: This position is classified as an 'evergreen role', meaning we continuously accept applications in this research domain. Given the high volume of applications, an immediate match for your skills and experience may not always be available. However, we encourage you to apply; we regularly review submissions and reach out as new opportunities arise. You are welcome to apply again after gaining more experience, but we ask that you refrain from applying more than once every six months. Additionally, specific postings for singular roles may be available for distinct projects or team needs, in which case you are welcome to apply directly in conjunction with this evergreen role.What You’ll DoDevelop and Optimize Recipes: Refine post-training recipes, encompassing various datasets, training stages, and hyperparameters, while assessing their impact on multiple performance metrics.Iterate on Evaluations: Engage in a continuous process of defining evaluation metrics, optimizing them, and recognizing their limitations. You will be accountable for enhancing performance metrics and ensuring they are meaningful.Debug and Analyze: During the fine-tuning of training configurations, you may encounter results that appear inconsistent. You will be responsible for troubleshooting and cultivating a deeper understanding to apply to subsequent challenges.Scale and Investigate: Assess and expand the capabilities of our models while exploring potential improvements.

OpenAI is hiring a Software Engineer for Post-Training Research in San Francisco. This position centers on improving the performance and capabilities of advanced machine learning models after their initial training phase. Role overview Work closely with a skilled team to explore new ways of strengthening AI systems. The focus is on researching and developing methods that push the boundaries of what these models can achieve once training is complete. Collaboration Expect to contribute to ongoing research efforts and share insights with colleagues who are passionate about advancing AI. Teamwork and knowledge exchange are key parts of this role. Location This position is based in San Francisco.

Join Mercor as a Data ScientistAt Mercor, we stand at the forefront of labor markets and artificial intelligence research. We collaborate with top-tier AI laboratories and businesses to infuse the human intelligence crucial for the evolution of AI.Our expansive talent network empowers frontier AI models, mirroring the way educators impart knowledge: sharing insights and experiences that transcend mere coding. Currently, our network boasts over 30,000 experts generating more than $2 million daily.We are pioneering a new work paradigm where specialized expertise drives AI progress. To realize this vision, we seek a dynamic, fast-paced, and dedicated team. You will collaborate with leading researchers, operators, and AI companies, playing a pivotal role in the systems that are reshaping society.As a profitable Series C company, Mercor is valued at $10 billion and operates from our new headquarters in San Francisco with an in-office work schedule five days a week.Your RoleIn your first year, you will implement analyses and experiments that enhance key product metrics, including match quality, time-to-hire, candidate experience, and revenue. Your responsibilities will include:Establishing north-star and feature-specific metrics for our ranking systems, interview analytics, and payout frameworks.Designing and executing A/B tests and quasi-experiments, translating results into product decisions within the same week.Creating source-of-truth dashboards and streamlined data models to enable teams to self-serve answers.Collaborating with engineers to instrument events, enhancing data quality and latency from ingestion to insights.Rapidly prototyping models (from baseline models to gradient boosting) to optimize matching and scoring.Assisting in the evaluation of LLM-powered agents through the design of rubrics, human-in-the-loop studies, and guardrail mechanisms.What Makes You a Great FitYou possess strong foundational skills in statistics, SQL, and Python, alongside projects you are eager to showcase. You adapt swiftly, frame inquiries, test hypotheses, and deliver results within a day, valuing clarity in communication as much as statistical significance. A keen interest in LLM evaluation, retrieval, and ranking is a plus; you will learn alongside professionals from renowned firms such as Jane Street, Citadel, Databricks, and Stripe.