Senior / Principal ML Research Scientist - Bayesian Optimization

Join Merge Labs, a pioneering research facility dedicated to merging biological and artificial intelligence to enhance human capabilities, agency, and experience. We aim to achieve this by crafting innovative brain-computer interfaces that communicate with the brain at high bandwidth, seamlessly integrate with cutting-edge AI, and prioritize safety and accessibility for all users.About the Team:At Merge Labs, we are on a mission to revolutionize brain-computer interfaces by leveraging advancements in synthetic biology, neuroscience, AI, and non-invasive imaging technologies. Our cross-functional data science team is situated at the convergence of computational modeling, neuroscience, and biomolecular engineering. This collaborative unit works closely with wet-lab scientists, automation specialists, and data engineers to develop machine learning frameworks that facilitate rapid molecule discovery and device enhancement.About the Role:We are seeking a talented Senior / Principal ML Scientist to architect and scale Bayesian optimization and reinforcement learning frameworks that guide molecular engineering initiatives through iterative design-build-test-learn (DBTL) cycles. You will start with a fresh approach to construct the company's closed-loop optimization infrastructure, establishing the data and modeling foundations that link experiments with these ML frameworks. Over time, you will transition prototypes into operational pipelines, significantly enhancing experimental throughput and discovery success across various biomolecular and neuroengineering sectors.Key Responsibilities:Develop the scientific and engineering framework for active learning and closed-loop optimization, encompassing data ingestion, ML modeling, and library design.Collaborate with wet-lab scientists to establish feasible optimization objectives while incorporating domain-specific priors and constraints.Create prototypes for representation learning and acquisition strategies utilizing both internal and public datasets; benchmark and validate the performance of models.Integrate machine learning models with experimental data streams, making them accessible to non-domain experts for broader utilization.Extend machine learning frameworks to accommodate multi-objective or constrained optimization challenges.Stay abreast of the latest advancements in Bayesian optimization, active learning, and reinforcement learning, and prototype innovative algorithms to enhance the company's capabilities.

At Merge Labs, we are at the forefront of research, dedicated to merging biological and artificial intelligence to enhance human capabilities and experiences. Our innovative journey involves crafting revolutionary brain-computer interfaces that communicate with the brain at high bandwidth, integrate with cutting-edge AI technologies, and are designed to be safe and accessible to all.About Our Team:We are pioneering the next wave of brain-computer interfaces by leveraging breakthroughs in synthetic biology, neuroscience, AI, and non-invasive imaging. To propel this mission, we are assembling a multidisciplinary data science team that intersects computational modeling, neuroscience, and biomolecular engineering. This team collaborates closely with wet-lab scientists, automation engineers, and data engineers to develop machine learning frameworks that expedite molecule discovery and enhance device optimization.Role Overview:We are seeking a Senior/Principal ML Scientist to architect and scale de novo design frameworks that steer molecular engineering initiatives through iterative design-build-test-learn (DBTL) cycles. Initially, you will establish the company’s closed-loop optimization infrastructure, developing the data and modeling frameworks that link experiments to these machine learning systems. As the role progresses, you will play a vital role in transitioning these prototypes into operational pipelines that significantly boost experimental throughput and success in biomolecular and neuroengineering domains.Key Responsibilities:Develop scientific and engineering frameworks in partnership with data engineering and MLOps for de novo design and closed-loop optimization, encompassing data ingestion, ML modeling, and library architecture.Work alongside wet-lab scientists to establish feasible optimization goals and incorporate domain-specific priors and constraints.Prototype de novo design frameworks utilizing both internal and public datasets; evaluate and validate model efficacy.Integrate ML models with experimental data streams, facilitating accessibility for non-expert users.Expand ML frameworks to accommodate multi-objective or constrained optimization challenges.Stay abreast of advancements in de novo design research and prototype innovative algorithms to enhance the company’s discovery and development processes.

Role overview The Principal Research Scientist – Scaling at Databricks leads research projects that advance how the company’s data analytics platform handles large workloads. This San Francisco-based role focuses on designing and improving algorithms that enable efficient large-scale data processing and machine learning. Collaboration is central, with regular work alongside engineering, product, and research teams. What you will do Lead research to develop algorithms that scale for data analytics applications. Work with colleagues across engineering, product, and research to strengthen machine learning capabilities. Use deep expertise to shape the direction and architecture of the Databricks platform. Drive new ideas and solutions that influence the future of data science and analytics at Databricks. Location This role is based in San Francisco, California.

At Sciforium, we are at the forefront of AI infrastructure, creating next-generation multimodal AI models and a proprietary high-efficiency serving platform. With substantial backing from AMD, our team is rapidly expanding to develop the complete stack necessary for cutting-edge AI models and real-time applications.About the RoleWe are on the lookout for a talented Senior Research Scientist with expertise in advanced AI and machine learning. This role entails spearheading innovative research projects focusing on large language models, generative media, model architecture, optimization, and scalable training systems. You will engage directly with contemporary ML frameworks, publish original research, and collaborate closely with engineering teams to transition impactful models into production. This position is perfect for a driven researcher excited about pioneering breakthroughs in AI.What You'll DoLead research initiatives in advanced machine learning topics such as LLMs, generative AI, foundational modeling, optimization strategies, diffusion models, and novel Transformer architectures.Design, implement, and assess new ML algorithms using frameworks like PyTorch and JAX.Conduct large-scale distributed training experiments utilizing multi-GPU/TPU systems and cutting-edge compute infrastructure.Enhance performance through debugging frameworks, optimizing speed, and refining training pipelines.Generate high-quality research outputs including academic papers, internal reports, patents, and reproducible code.Work collaboratively with engineering and product teams to convert research prototypes into robust production systems.Stay updated with the latest research advancements to incorporate state-of-the-art techniques into Sciforium's AI roadmap.Mentor junior researchers and actively contribute to fostering a world-class AI research culture.

At Databricks, we are dedicated to empowering data teams to tackle the world's most challenging problems, from detecting security threats to advancing cancer drug development. We achieve this by offering the premier data and AI platform, allowing our customers to concentrate on their mission-critical challenges. The Mosaic AI organization assists companies in developing AI models and systems utilizing their own data, employing technologies that range from training large language models (LLMs) from the ground up to employing advanced retrieval methods for enhanced generation. We pride ourselves on pushing the boundaries of science and operationalizing our innovations. Mosaic AI believes that a company’s AI models hold intrinsic value, akin to any other core intellectual property, and that superior AI models should be accessible to all. Job Overview As a research engineer in the Scaling team, you will stay abreast of the latest advancements in deep learning and pioneer new methodologies that surpass the current state of the art. You will collaborate with a diverse team of researchers and engineers, sharing insights and expertise. Most importantly, you will be passionate about our customers, striving to ensure their success in implementing cutting-edge LLMs and AI systems by translating our scientific knowledge into practical applications. Your Impact Enhance performance through innovative optimization techniques, including kernel fusion, mixed precision, memory layout optimization, tiling strategies, and tensorization tailored for training-specific patterns. Design, implement, and optimize high-performance GPU kernels for training workloads, including attention mechanisms, custom layers, gradient computations, and activation functions, specifically for NVIDIA architectures. Create and implement distributed training frameworks for large language models, incorporating parallelism strategies (data, tensor, pipeline, ZeRO-based) and optimized communication patterns for gradient synchronization and collective operations. Profile, debug, and optimize comprehensive training workflows to pinpoint and resolve performance bottlenecks, utilizing memory optimization techniques such as activation checkpointing, gradient sharding, and mixed precision training.

About FaireFaire is revolutionizing the wholesale marketplace, advocating for local businesses by connecting independent retailers worldwide. We believe in the power of local entrepreneurship, where independent shops can thrive and compete against giants like Walmart and Amazon. Our innovative technology, data analytics, and machine learning empower these businesses to discover unique products that resonate with their communities. By supporting local economies, we are committed to driving positive change globally. Join us in our mission to empower local businesses and make a difference!About this RoleAs a vital member of our Ads Data team, you will play a crucial role in developing cutting-edge advertising solutions for the wholesale sector. You will influence the design and functionality of our auction system, focusing on bidding strategies, pacing, pricing, and budget optimization. Utilizing our proprietary conversion data, you will train models that yield significant business outcomes for our partners. This is an exceptional opportunity to make a substantial impact in a rapidly growing division that is a key strategic focus for Faire.Collaborating with engineers, product managers, and designers, you will contribute to systems that not only demonstrate technical sophistication but also enhance profitability for both our brands and Faire.

OverviewPluralis Research is at the forefront of innovation in Protocol Learning, specializing in the collaborative training of foundational models. Our approach ensures that no single participant ever has or can obtain a complete version of the model. This initiative aims to create community-driven, collectively owned frontier models that operate on self-sustaining economic principles.We are seeking experienced Senior or Staff Machine Learning Engineers with over 5 years of expertise in distributed systems and large-scale machine learning training. In this role, you will design and implement a groundbreaking substrate for training distributed ML models that function effectively over consumer-grade internet connections.

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.

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.

OverviewPluralis Research is at the forefront of Protocol Learning, innovating a decentralized approach to train and deploy AI models that democratizes access beyond just well-funded corporations. By aggregating computational resources from diverse participants, we incentivize collaboration while safeguarding against centralized control of model weights, paving the way for a truly open and cooperative environment for advanced AI.We are seeking a talented Machine Learning Training Platform Engineer to design, develop, and scale the core infrastructure that powers our decentralized ML training platform. In this role, you will have ownership over essential systems including infrastructure orchestration, distributed computing, and service integration, facilitating ongoing experimentation and large-scale model training.ResponsibilitiesMulti-Cloud Infrastructure: Create resource management systems that provision and orchestrate computing resources across AWS, GCP, and Azure using infrastructure-as-code tools like Pulumi or Terraform. Manage dynamic scaling, state synchronization, and concurrent operations across hundreds of diverse nodes.Distributed Training Systems: Design fault-tolerant infrastructure for distributed machine learning, including GPU clusters, NVIDIA runtime, S3 checkpointing, large dataset management and streaming, health monitoring, and resilient retry strategies.Real-World Networking: Develop systems that simulate and manage real-world network conditions—such as bandwidth shaping, latency injection, and packet loss—while accommodating dynamic node churn and ensuring efficient data flow across workers with varying connectivity, as our training occurs on consumer nodes and non-co-located infrastructure.

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 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.

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.

About FaireFaire is a revolutionary online wholesale marketplace that champions the belief that the future of retail is local. Independent retailers worldwide generate more revenue collectively than giants like Walmart and Amazon, yet they often lack the resources to compete. At Faire, we leverage cutting-edge technology, data analytics, and machine learning to empower this vibrant community of entrepreneurs globally. Imagine your favorite local boutique — we facilitate their access to exceptional products from around the globe. By providing the right tools and insights, we aim to level the playing field, enabling small businesses everywhere to thrive against e-commerce giants.By nurturing the growth of independent businesses, Faire is making a significant economic impact in local communities, worldwide. We seek intelligent, resourceful, and passionate individuals to join us in driving the shop local movement. If you believe in the power of community, we invite you to become a part of ours.About this RoleAs a Senior Applied AI/ML Scientist within the Search group, you will play a pivotal role in shaping the technical vision and machine-learning strategy that drives one of our core growth areas: Search. You will enhance real-time Search and Recommendation systems that deliver next-generation shopping experiences.You'll be at the forefront of algorithm development, integrating large language models, natural language processing, query understanding, deep learning, transformer-based sequential modeling, graph neural networks, and structured behavioral data to return hyper-relevant, personalized product suggestions for every user query.This is a unique opportunity to influence end-to-end personalization in a high-scale, multi-modal environment while collaborating closely with a talented team of scientists and engineers.

Join our dynamic team at Amgen as a Principal Scientist - Biotransformation in San Francisco. In this pivotal role, you will lead innovative research focusing on biotransformation processes, contributing to the development of cutting-edge therapeutics. Your expertise will drive scientific advancements and support our mission to improve the lives of patients worldwide.

About FaireFaire is a dynamic online wholesale marketplace that champions the belief in a localized future. Independent retailers worldwide are generating more revenue than industry giants like Walmart and Amazon combined; however, on their own, they remain small. At Faire, we harness the power of technology, data, and machine learning to connect this vibrant community of entrepreneurs globally. Imagine your favorite local boutique — we empower them to discover exceptional products from around the world to offer in their stores. With the right tools and insights, we believe we can create a level playing field so that small businesses everywhere can compete with the massive retail and e-commerce entities.By fostering the growth of independent businesses, Faire positively impacts local economies globally. We seek intelligent, resourceful, and passionate individuals to join us in fueling the shop local movement. If you believe in community, we invite you to become a part of ours.About this RoleThe Ads Data team is developing the next generation of advertising solutions for the wholesale sector. As an integral member of this team, you will spearhead the machine learning algorithm strategy and system architecture that drives one of the most vital components for customer value and corporate growth—Search Ads. You will lead the development of real-time systems that determine which ads to display for a given query, their placement, and how to optimize for relevance, marketplace health, and advertiser performance. This role parallels many technical expectations of Faire's organic Search positions (including modern NLP/LLMs, query understanding, real-time ranking), while functioning within an advertising context featuring auctions, budgets, and pacing constraints.You will work at the cutting edge of algorithms, blending large language models, natural language processing, query understanding, deep learning, and structured behavioral data to deliver highly relevant sponsored results for any query.

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.

Team Overview The infrastructure team at OpenAI manages the core systems that support AI workloads worldwide. As OpenAI expands its compute capabilities across company-owned data centers, cloud environments, and strategic partnerships, the need for careful planning and resource management grows. Reliable and cost-effective compute operations depend on this foundation. The Compute Optimization group operates at the intersection of engineering, operations, finance, and infrastructure strategy. This team develops models, decision tools, and planning systems to improve how compute resources are scheduled, deployed, and scaled as global needs shift. Role Overview OpenAI is hiring a Compute Optimization Researcher/Engineer to help maximize the use of compute capacity across the organization. This role addresses complex optimization challenges related to capacity allocation, demand forecasting, cluster planning, workload placement, and infrastructure utilization. Work includes building mathematical models, developing software systems, and collaborating with other teams to improve planning and use of compute resources. Areas of focus span GPU clusters, networking, storage, and data center infrastructure. Candidates with experience in operations research, optimization, applied mathematics, infrastructure systems, or large-scale capacity planning will be well-suited for this position. Location and Work Model This position is based in San Francisco, CA. OpenAI follows a hybrid schedule with three days per week in the office. Relocation assistance is offered.

Join Scale AI's ML platform team (RLXF) as a Machine Learning Research Engineer, where you will play a pivotal role in developing our advanced distributed framework for training and inference of large language models. This platform is vital for enabling machine learning engineers, researchers, data scientists, and operators to conduct rapid and automated training, as well as evaluation of LLMs and data quality.At Scale, we occupy a unique position in the AI landscape, serving as an essential provider of training and evaluation data along with comprehensive solutions for the entire ML lifecycle. You will collaborate closely with Scale's ML teams and researchers to enhance the foundational platform that underpins our ML research and development initiatives. Your contributions will be crucial in optimizing the platform to support the next generation of LLM training, inference, and data curation.If you are passionate about driving the future of AI through groundbreaking innovations, we want to hear from you!