Principal Research Scientist - AI Scaling & Optimization

Join Databricks as a Principal Research Scientist specializing in AI Scaling & Optimization. In this pivotal role, you'll lead groundbreaking research initiatives aimed at enhancing the scalability and efficiency of AI models. Collaborate with cross-functional teams to translate innovative algorithms into practical applications that drive business value.As a…

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.

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.

Join Scale Labs as a Research Scientist – AI Controls and MonitoringScale AI is at the forefront of data solutions for pioneering AI enterprises, playing a crucial role in understanding and protecting AI models and systems. Our new team within Scale Labs is dedicated to policy research, bridging the divide between AI advancements and global policymakers to make informed and scientific decisions regarding AI risks and capabilities.We tackle complex challenges in agent robustness, AI control mechanisms, and risk evaluations, assisting governments, industries, and the public in understanding and mitigating AI risks while fostering AI adoption. Our collaborative efforts involve partnerships across various sectors, including industry, public entities, and academia, and we regularly share our findings with the community. We are looking for passionate researchers to help us fulfill this vision.As a Research Scientist specializing in AI Controls and Monitoring, you will develop methodologies, systems, and experiments to ensure advanced AI models and agents stay aligned with their intended goals, even in critical or adversarial situations. Your responsibilities may include:Creating monitoring strategies and observability techniques to track AI behavior in real-time, identifying and flagging deviations, emergent capabilities, or anomalous outputs;Investigating layered control mechanisms, including fail-safes, oversight protocols, and intervention strategies to redirect AI systems when risks arise;Designing red-team simulations to uncover vulnerabilities in oversight and control frameworks, and implementing measures to address identified gaps;Collaborating with policymakers, engineers, and researchers to establish standards and benchmarks for AI monitoring and escalation.

At Scale AI, we are at the forefront of artificial intelligence, driving innovation through our advanced data, infrastructure, and tooling that empower the most sophisticated models worldwide. Our teams thrive at the intersection of pioneering research, extensive engineering, and practical deployment, collaborating with leading labs, enterprises, and government entities to explore the vast potential of Generative AI. As AI technology evolves from static models to dynamic, intelligent systems, Scale AI is dedicated to establishing the essential research foundations, evaluation methodologies, and reinforcement learning infrastructure that will shape this transformative era. Join our high-impact research organization, where you will contribute to advancing large language models, post-training evaluation, and agent-based reinforcement learning environments, influencing the future of AI development and implementation. As the Research Scientist Manager, you will spearhead a distinguished team of research scientists and engineers, define the strategic research roadmap, and oversee projects from initial prototyping to final deployment. You will excel in a fast-paced environment, harmonizing deep technical leadership with effective people management, visionary goal setting, and successful delivery.

Join Scale Labs as a Research Scientist — Agent RobustnessScale is the premier partner for data and evaluation within the forefront of AI innovation, playing a crucial role in understanding and safeguarding AI models and systems. Building on our extensive expertise, Scale Labs has initiated a dedicated team focused on policy research, aiming to connect AI research with global policymakers to facilitate informed, scientifically grounded decisions regarding AI risks and capabilities.Our research addresses complex challenges in agent robustness, AI control protocols, and AI risk evaluations, empowering governments, industries, and the public to comprehend and mitigate AI risks while promoting AI adoption. This team collaborates across various sectors, including industry, public services, and academia, and regularly disseminates our findings. We are actively inviting skilled researchers to contribute to this vision.As a Research Scientist specializing in Agent Robustness, you will tackle foundational challenges in creating AI agents that are both safe and aligned with human values. Your responsibilities may include:Investigating the science behind AI agent capabilities, focusing on safety, risk factors, and benchmarking methodologies.Designing and building testing harnesses to evaluate AI agents' tendencies to engage in harmful actions under user pressure or environmental manipulation.Creating exploits and mitigations for new failure modes that emerge as AI agents gain capabilities such as coding, web browsing, and computer usage.Characterizing and developing mitigations for potential failure modes or broader risks involving multiple interacting AI agents.

Join Scale AI as a Research Scientist — Frontier Risk EvaluationsAt Scale AI, we are at the forefront of data and evaluation services for pioneering AI technologies. Our mission is to ensure the safe and effective deployment of AI systems by bridging the gap between advanced AI research and global policy frameworks. With the launch of Scale Labs, we are assembling a dedicated team focused on policy research to empower governments and industry leaders with scientific insights regarding AI risks and functionalities.This team addresses complex challenges in agent robustness, AI control mechanisms, and risk assessments to facilitate a comprehensive understanding of AI risks, while promoting its responsible adoption across various sectors. We are eager to welcome skilled researchers who are passionate about shaping the future of AI.As a Research Scientist specializing in Frontier Risk Evaluations, you will be responsible for designing evaluation metrics, harnesses, and datasets to assess the risks associated with cutting-edge AI systems. Your role may involve:Developing harnesses to evaluate AI models for potential security vulnerabilities and other high-risk behaviors.Collaborating with government entities and research labs to design evaluations that mitigate risks posed by advanced AI technologies.Publishing evaluation methodologies and drafting technical reports aimed at informing policymakers.

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.

About Scale AI At Scale AI, we are committed to propelling the advancement of AI technologies. For over eight years, we have been a pioneer in the AI data sector, supporting groundbreaking innovations in areas such as generative AI, defense solutions, and autonomous driving. Following our recent Series F funding round, we are enhancing access to premium data to accelerate the journey towards Artificial General Intelligence (AGI). Building on our legacy of model evaluation for both enterprise and governmental clients, we are expanding our capabilities to establish new benchmarks for evaluations in both public and private domains. About This Role This position is at the leading edge of AI research and practical implementation, concentrating on reasoning within large language models (LLMs). The successful candidate will investigate critical data types vital for evolving LLM-based agents, including browser and software engineering agents. You will significantly influence Scale’s data strategy by pinpointing optimal data sources and methodologies to enhance LLM reasoning. To excel in this role, you will require a profound understanding of LLMs, planning algorithms, and fresh approaches to agentic reasoning, alongside inventive solutions to challenges in data generation, model interaction, and evaluation. Your contributions will lead to transformative research on language model reasoning, facilitate collaboration with external researchers, and engage closely with engineering teams to translate cutting-edge advancements into scalable, real-world applications.

About Retell AI Retell AI develops advanced voice AI technology for call centers, using first-principles approaches to create intelligent voice agents. These agents help businesses manage sales, support, and logistics communications while reducing reliance on large human teams. The company has reached $36M ARR in just 18 months, backed by Y Combinator and Alt Capital. With a team of 20, Retell AI is building a comprehensive customer experience platform, aiming for AI-powered contact centers by 2026. The vision: intelligent agents that execute, monitor, and improve customer interactions with minimal human oversight. Named a top 50 AI app by a16z: https://tinyurl.com/5853dt2x Ranked #4 on Brex's Fast-Growing Software Vendors of 2025: https://www.brex.com/journal/brex-benchmark-december-2025 Featured among top startups: https://leanaileaderboard.com/ Role Overview: Research Scientist - Voice AI Innovation This role centers on advancing machine learning for human-like voice agents in real-world settings. The Research Scientist will explore new methods in large language models (LLMs) and audio models, design evaluation techniques, and prototype systems that improve reasoning, reduce latency, and enhance conversational quality. The work involves open-ended ML challenges, rapid experimentation, and direct influence on the performance and cognitive abilities of voice AI systems at scale. Location San Francisco Bay Area

About GranicaGranica is an innovative AI research and infrastructure firm dedicated to creating reliable, steerable representations of enterprise data.We establish trust through Crunch, a policy-driven health layer optimizing large tabular datasets for efficiency, reliability, and reversibility. Utilizing this foundation, we are developing Large Tabular Models—systems designed to learn cross-column and relational structures, delivering trustworthy answers and automation with integrated provenance and governance.Our MissionCurrent AI capabilities are hindered not only by model design but also by the inefficiencies of the data that supports it. At scale, each redundant byte, poorly organized dataset, and inefficient data pathway contributes to significant costs, latency, and energy waste.Granica’s mission is to eliminate these inefficiencies. We leverage groundbreaking research in information theory, probabilistic modeling, and distributed systems to craft self-optimizing data infrastructure: systems that continually enhance how information is represented and utilized by AI.Led by Prof. Andrea Montanari from Stanford, Granica’s Research group merges advances in information theory with learning efficiency in large-scale distributed systems. We collectively believe that the next significant leap in AI will originate from innovations in efficient systems, rather than merely larger models.Granica is at the forefront of developing a new category of structured AI models: foundational models designed to learn and reason from the relational, tabular, and structured data that drives the global economy. While many focus on unstructured text or media, we are venturing into the next frontier: systems capable of comprehending and reasoning over structured information.Your ContributionsCreate and prototype algorithms that form the core of structured AI, enhancing representation learning and efficient information modeling for enterprise and tabular data at petabyte scale.Develop adaptive learners merging statistical learning theory with systems optimization at scale, contributing to a new generation of foundational models for structured information.Design architectures that unify symbolic, relational, and neural components, enabling AI systems to reason directly over structured enterprise data.Construct cost models and optimization frameworks that enhance the efficiency of structured learning, both computationally and economically.

AI Research ScientistAt Substrate, we are tackling a critical technological challenge that impacts the United States. Positioned at the crossroads of advanced manufacturing and innovative physics, our mission is to develop transformative technologies that will revolutionize the semiconductor industry and bolster America's technological dominance. Our team comprises top-tier scientists, engineers, and technical specialists dedicated to pushing the boundaries of technology for the benefit of the nation.As an AI Research Scientist, you will play a key role in enhancing and accelerating research and development processes by harnessing machine learning techniques for scientific simulations and modeling. You will also focus on establishing internal AI capabilities throughout our organization. This position merges cutting-edge physics with artificial intelligence, requiring hands-on development of AI-enhanced tools that facilitate groundbreaking research. You will also contribute to building the infrastructure and expertise required for our technical teams to effectively use AI in their workflows. Whether you are a physicist who has adopted machine learning or an AI expert with a solid scientific background, you will be instrumental in shaping our approach to utilizing AI to expedite our internal R&D efforts.

AI Research ScientistOverviewJoin Physical Superintelligence, an innovative startup rooted in prestigious institutions such as Harvard, MIT, Johns Hopkins, Oxford, the Institute for Advanced Study, and the Perimeter Institute. We are at the forefront of building AI systems designed to uncover groundbreaking insights in physics on a grand scale. We are in search of talented AI researchers dedicated to developing reinforcement learning agents and training frameworks that propel scientific discovery.Key Responsibilities- Develop and optimize AI systems aimed at physics discovery, collaborating with physicists on verification harnesses and engineers on training infrastructure.- Address critical AI research questions related to agent learning in physics reasoning, action space design for scientific exploration, reward structure development, and scalable training systems.- Construct and train reinforcement learning agents leveraging cutting-edge methodologies such as PPO, SAC, MuZero, and multi-agent self-play.- Design agent architectures tailored for physics reasoning and scientific tool utilization.- Execute training curricula and reward structures for discovery tasks.- Establish evaluation workflows and benchmarks to assess physics reasoning capabilities.- Develop instrumentation to analyze agent behavior and learning dynamics.- Collaborate closely with physicists and engineers to refine system design and architecture.Candidate ProfileWe are looking for candidates with a strong background in developing agents and training models using reinforcement learning. Proficiency in modern machine learning frameworks and experience with distributed training systems is essential, alongside a proven track record of deploying effective AI systems.Essential Skills:- Practical experience with contemporary reinforcement learning algorithms including PPO, SAC, MuZero, and multi-agent self-play.- Proficient in PyTorch or JAX, with hands-on experience in distributed training using Ray, XLA, or Accelerate, and familiarity with modern pretraining workflows.Preferred Background:- A strong foundation in physics or mathematics that enhances intuition for physical reasoning and mathematical modeling.- Experience applying agents in simulators, games, scientific tool use, or benchmark design employing rigorous experimental methodologies.

About Scale AI At Scale AI, we are dedicated to propelling the advancement of AI applications. Over the past eight years, we have established ourselves as the premier AI data foundry, supporting groundbreaking innovations in fields such as generative AI, defense technologies, and autonomous vehicles. Following our recent Series F funding round, we are intensifying our efforts to harness frontier data, paving the way toward achieving Artificial General Intelligence (AGI). Our work with enterprise clients and governments has enhanced our model evaluation capabilities, allowing us to expand our offerings for both public and private evaluations. About the ACE Team The Agent Capabilities & Environments (ACE) team, a vital part of Scale’s Research organization, unites customer-focused Researchers and Applied AI Engineers. Our primary mission is to conduct research on agent environments and reinforcement learning reward signals, benchmark autonomous agent performance in real-world contexts, and develop robust data programs aimed at enhancing the capabilities of Large Language Models (LLMs). We are committed to creating foundational tools and frameworks for evaluating models as agents, focusing on autonomous agents that interact dynamically with a wide range of external environments, including code repositories and GUI interfaces. About This Role This position sits at the cutting edge of AI research and its practical applications, concentrating on the data types necessary for the development of state-of-the-art agents, including browser and software engineering agents. The ideal candidate will investigate the data landscape required to propel intelligent and adaptable AI agents, steering the data strategy at Scale to foster innovation. This role demands not only expertise in LLM agents and planning algorithms but also creative problem-solving skills to tackle novel challenges pertaining to data, interaction, and evaluation. You will contribute to influential research publications on agents, collaborate with customer researchers, and partner with the engineering team to transform these advancements into scalable real-world solutions.

Join Zyphra as a Research Engineer specializing in AI Performance and Kernel Optimization. In this role, you will work at the forefront of AI technologies, developing and optimizing kernel solutions that enhance the performance of our systems. You will collaborate with cross-functional teams, leveraging your expertise to drive innovation and efficiency.

About Retell AI Retell AI builds voice AI technology that helps businesses transform their call center operations. In just 18 months, thousands of companies have adopted Retell’s AI voice agents to streamline sales, support, and logistics, work that once required large human teams. Backed by investors including Y Combinator and Alt Capital, Retell has grown annual recurring revenue from $5M to $36M with a focused team of 20. The company’s goal for 2026: a modern customer experience platform where AI powers entire contact centers. Retell is developing AI “workers” that can serve as frontline agents, quality assurance analysts, and managers, handling, evaluating, and improving customer interactions on their own. Named a top 50 AI app by a16z: https://tinyurl.com/5853dt2x Ranked #4 on Brex’s Fast-Growing Software Vendors of 2025: https://www.brex.com/journal/brex-benchmark-december-2025 Featured on the Lean AI Leaderboard: https://leanaileaderboard.com/ Role Overview: Research Scientist – LLM Retell AI is hiring a Research Scientist focused on large language models (LLMs) and audio processing. This role suits machine learning researchers who want to push the boundaries of real-time AI and see their work in production. What You Will Do Investigate new approaches in large language models and audio processing for human-like voice agents Design and implement evaluation methods for complex, real-world conversational systems Prototype systems to improve reasoning, reduce latency, and enhance conversation quality Work closely with engineering and product teams to bring research advances into production Impact Research at Retell directly shapes the capabilities of voice AI agents for thousands of businesses. The work blends advanced research with practical deployment, improving how customers interact with automated systems across industries. Location This position is based in the San Francisco Bay Area.

Join AbbVie as a Principal Research Scientist I in our Biologics Screening team within Discovery Biotherapeutics. In this pivotal role, you will lead cutting-edge research initiatives focused on biologics screening methodologies that drive our biotherapeutic development pipeline. You will collaborate with a multidisciplinary team of scientists to innovate and optimize screening strategies, ensuring the identification of high-potential therapeutic candidates.

Artificial Intelligence is revolutionizing every aspect of our lives. At Scale AI, we are dedicated to accelerating the advancement of AI applications across industries. For nearly a decade, we have established ourselves as a premier AI data foundry, powering groundbreaking innovations in AI, including generative AI, defense systems, and autonomous technologies. With our recent investment from Meta, we are committed to enhancing our state-of-the-art post-training algorithms to achieve unparalleled performance for complex agents serving enterprises globally. The Enterprise ML Research Lab is at the forefront of this AI evolution. Our team develops a suite of proprietary research, tools, and resources tailored for our enterprise clients. As a Machine Learning Research Engineer on the Data Foundation team, you will engage in pioneering research to optimize the data flywheel that drives our entire machine learning ecosystem. Your work will involve exploring synthetic environments, defining tasks, building agents for trace analysis, and contributing to a cutting-edge framework that automates agent building through advanced evaluation techniques. You will create top-tier agents that deliver state-of-the-art results by leveraging sophisticated post-training and agent-building algorithms. If you are passionate about influencing the future of Generative AI, we encourage you to apply!

Artificial Intelligence (AI) is becoming increasingly crucial across all sectors of society. At Scale AI, our mission is to expedite the advancement of AI applications. With nine years of experience, we have established ourselves as the leading AI data foundry, facilitating groundbreaking developments in AI, including generative AI, defense applications, and autonomous vehicles. Following our recent investment from Meta, we are committed to enhancing our capabilities by developing cutting-edge post-training algorithms that are essential for optimizing complex agents in enterprises globally.The Enterprise ML Research Lab is at the forefront of this AI revolution. We are dedicated to crafting a suite of proprietary research tools and resources that cater to all of our enterprise clients. As a Machine Learning Research Engineer focusing on Agents, you will apply our Agent Reinforcement Learning (RL) training and building algorithms to real-world enterprise datasets across our clients and benchmarks. Your role will involve developing top-tier Agents that achieve state-of-the-art results through a blend of post-training and agent-building algorithms.If you are passionate about influencing the trajectory of the modern Generative AI movement, we would love to hear from you!

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.