Researcher, Robustness & Safety Training

About the TeamThe Safety Systems team is dedicated to ensuring the responsible deployment of our advanced AI models for societal benefit. We lead OpenAI's mission to develop and implement safe AGI, prioritizing transparency and trust in our AI systems.The Model Safety Research team is focused on pioneering research to enhance the robustness and safety of AI …

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.

About Our TeamThe Safety Systems team at OpenAI is dedicated to advancing safety protocols to ensure our cutting-edge models can be deployed responsibly, ultimately benefiting society. We are at the forefront of OpenAI's commitment to creating and deploying safe Artificial General Intelligence (AGI), fostering a culture rooted in trust and transparency.The Pretraining Safety team aspires to develop safer, more capable base models while facilitating early and reliable safety assessments during the training phase. Our objectives include:Establishing upstream safety evaluations to track the emergence of unsafe behaviors and goals;Creating safer priors through strategic pretraining and mid-training interventions that enhance downstream alignment;Designing safe-by-design architectures that improve control over model capabilities.Additionally, we conduct foundational research to comprehend how behaviors develop, generalize, and can be accurately measured throughout the training process.About the RoleThe Pretraining Safety team is trailblazing the integration of safety into models prior to their post-training and deployment stages. In this position, you will engage with the complete model development lifecycle, focusing on pre-training:Identifying safety-relevant behaviors as they emerge in base models;Assessing and mitigating risk without waiting for extensive training runs;Designing architectures and training setups that prioritize safer behavior;Enhancing models by integrating comprehensive, early safety signals.Our collaborative efforts span across OpenAI’s safety ecosystem—from Safety Systems to Training—to ensure our safety foundations are robust, scalable, and grounded in real-world considerations.Your Responsibilities Will Include:Developing innovative techniques to predict, measure, and assess unsafe behavior in early-stage models;Crafting data curation strategies that refine pretraining priors and mitigate downstream risk;Investigating safe-by-design architectures and training configurations to enhance controllability;Collaborating with cross-functional teams to ensure adherence to safety standards.

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

Role OverviewAt Intrinsic Safety, we are pioneering the development of AI systems capable of making critical decisions in high-stakes environments such as risk investigations, fraud detection, and identity verification. Our dedicated team in San Francisco is at the forefront of tackling complex challenges where traditional AI solutions often fall short.We are in search of a Research Engineer to play a pivotal role in shaping our model evaluation strategies. You will be responsible for creating benchmarks, datasets, and evaluation frameworks that accurately assess our systems’ performance in real-world scenarios. This position bridges research, product development, and engineering, focusing on rigorous evaluations that reflect actual customer workflows and identify key failure points to propel the next generation of AI advancements.

About Our TeamThe Alignment Training team at OpenAI focuses on understanding how advanced models develop lasting behavioral patterns throughout the training process. We investigate which behaviors can be influenced during the pre-training, mid-training, and post-training phases; create the necessary data, objectives, and evaluations to guide these behaviors; and assess whether the resulting actions represent a general capability or a byproduct of the training environment.Our research encompasses synthetic data development, various training stages, model behavior analysis, and performance evaluation. We explore how models grasp user intentions, adhere to instructions, reason effectively, demonstrate honesty, and maintain reliability in novel situations. Our ultimate aim is to foster desirable behaviors early in training, reinforce them throughout, and ensure their consistency in real-world applications.About This PositionWe are seeking a seasoned researcher with profound expertise in large-scale model training, synthetic data creation, or evaluation processes, who is passionate about exploring how training decisions influence aligned behaviors in state-of-the-art models.In this role, you will define the research agenda for alignment training: outlining the behaviors we aspire for models to acquire, designing data and training strategies to cultivate them, and developing evaluation mechanisms to verify the breadth, strength, and durability of those behaviors. The ideal candidate will excel at translating vague behavioral inquiries into structured experimental plans: devising hypotheses, creating interventions, establishing pipelines, conducting experiments, and analyzing results for authenticity.This position is particularly suited for individuals eager to engage closely with the core model training framework, where decisions regarding data, objectives, and evaluations critically influence the alignment of deployed systems.Key Responsibilities:Innovate synthetic data methods that instill higher-level behavioral tendencies in models, such as comprehending user intent, consistently following instructions, clear reasoning, honesty, and alignment with defined goals and constraints.Analyze the impact of pre-training, mid-training, and post-training on subsequent model behavior, identifying the most effective interventions for each phase.Develop evaluation loops that link model behavior back to training data and objectives, enabling quicker iterations and clearer feedback.

About Our TeamJoin the Safety Systems team at OpenAI, where we are dedicated to ensuring that our cutting-edge models are deployed safely in the real world, positively impacting society. We are at the forefront of OpenAI's mission to develop and implement safe Artificial General Intelligence (AGI), emphasizing a culture of trust, transparency, and responsibility in AI.The Safety Research team is focused on advancing our capabilities to implement robust and safe behaviors in AI models and systems. As we make strides in AI capabilities, our safety approaches must evolve to effectively address the changing landscape of risks. This vigilance is essential not only for preventing harmful misuse but also for ensuring that potential misalignments do not result in adverse outcomes. Our research is grounded in current methodologies while also being adaptable to future systems.As we expand our team, we are looking for innovative research methods that enhance safety for AGI and beyond. This includes exploratory research into improving safety common sense and generalizable reasoning, developing evaluations to identify misalignment or hidden objectives of AI, and creating new strategies to support human oversight during long-term tasks.About the RoleIn your capacity as a Technical Lead, you will spearhead our strategic initiatives aimed at mitigating potential risks arising from misalignment or significant errors. Your responsibilities will encompass:Establishing visionary goals and milestones for new research endeavors, alongside crafting rigorous evaluations to monitor progress.Leading or driving research into new exploratory areas to validate the feasibility and scalability of our safety approaches.Collaborating across safety research and related teams to ensure that diverse technical strategies converge to deliver robust safety outcomes.We seek individuals with a proven track record in practical research concerning safety and alignment, particularly within the realms of AI and large language models (LLMs), who have successfully led substantial research initiatives in the past.This role is situated in San Francisco, CA, with a hybrid work model of three days in the office each week. We also offer relocation assistance to new employees.

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.

About Our TeamAt OpenAI, our Safety Systems team plays a crucial role in ensuring that our advanced models are safely deployed in real-world scenarios, amplifying the benefits to society. We are at the forefront of OpenAI's mission to develop and implement safe Artificial General Intelligence (AGI), emphasizing our unwavering commitment to AI safety, trust, and transparency.The Safety Oversight Research team is dedicated to enhancing our ability to exercise oversight over cutting-edge AI models. Our focus is on leveraging novel machine learning research in human-AI collaboration, reasoning, robustness, and scalable oversight to match the evolving capabilities of our models. We invest significantly in pioneering methods for identifying and mitigating AI misuse and misalignment.Our mission is to learn from real-world deployments and ensure that the benefits of AI are maximized, all while maintaining responsible and safe usage of this transformative tool.About the PositionWe are in search of a passionate and experienced senior researcher in AI safety. This pivotal role will shape the research agenda for overseeing safe AGI and spearhead initiatives focused on identifying and mitigating misuse and misalignment in our AI systems. You will significantly influence the future landscape of safe AI at OpenAI, directly impacting our mission of building and deploying AGI responsibly.In this position, your responsibilities will include:Designing and enhancing AI monitoring models to identify and mitigate known and emerging patterns of misuse and misalignment.Establishing research directions and strategies to enhance the safety, alignment, and robustness of our AI systems.Evaluating and architecting effective red-teaming processes to assess the overall robustness of our safety systems, pinpointing areas for improvement.Conducting research to advance models’ reasoning capabilities concerning human values, applying these insights to practical safety issues.Collaborating across departments, including Trust and Safety, legal, policy, and other research teams, to ensure our products adhere to the highest safety standards.You Might Be a Great Fit if You:Are passionate about advancing AI safety and possess a strong background in safety research.Have a proven track record in machine learning research and its application to safety and oversight.Thrive in collaborative environments and enjoy working with diverse teams.

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.

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 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.

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

About Our Team:At the Safety Systems organization, we are dedicated to ensuring that OpenAI's most advanced models are developed and deployed with responsibility. We focus on creating evaluations, safeguards, and robust safety frameworks that enable our models to perform as intended in real-world environments.About the Role:We are in search of a Researcher specializing in Privacy-Preserving Safety to lead the design and development of next-generation safety systems that prioritize user privacy for cutting-edge AI models. This position operates at the crossroads of AI safety, security, and privacy, aiming to create auditable mechanisms that ensure effective harm detection and mitigation without compromising sensitive user data.Your mission will be to define and implement frameworks for identifying and mitigating frontier risks (such as bioweapon instructions, malware creation, and self-harm risks), while upholding stringent privacy protections even in adversarial contexts.This role is pivotal to our vision of scaling automated privacy-preserving safety systems to minimize potential harms and reduce the necessity for human intervention.You'll engage in critical challenges, including privacy-preserving monitoring, algorithmic auditing, secure enclaves, and developing adversarially resilient safety enforcement protocols, ensuring our safety systems expand without losing user trust.Key Responsibilities:Design and implement architectures that prioritize privacy in the detection and mitigation of harmful model behaviors.Establish frameworks for the auditable private identification of high-risk content (including jailbreaks and cyber threats).Create strict, auditable mechanisms activated solely by harm signals.Lead the advancement of automated safety systems that maintain privacy across all levels.You Will Excel in This Role If You:Are a researcher deeply passionate about privacy, security, and AI safety, driven to develop systems that are both trustworthy and scalable.Possess a PhD or equivalent experience in Computer Science, Cryptography, Security, Machine Learning, or related disciplines.Demonstrate the ability to transform ambiguous problem spaces into clear frameworks and executable systems.Exhibit a strong foundation in privacy-preserving technologies and methodologies.

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.

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 Anthropic as a Research Lead for Training Insights, where you'll spearhead innovative research initiatives that shape the future of AI training methodologies. As part of our dynamic team, you will collaborate with cross-functional experts to extract meaningful insights from training data, driving improvements in AI models. Your expertise will be vital in enhancing our understanding of AI performance and guiding strategic decisions.

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.

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.