Forward-Deployed Research Engineer at primeintellect | San Francisco

Join the Center for AI Safety (CAIS), a premier research and advocacy institution dedicated to minimizing large-scale societal risks associated with artificial intelligence. We tackle the most pressing challenges in AI through innovative technical research, community-building initiatives, and active policy engagement, alongside our sister organization, the Center for AI Safety Action Fund.As a Research Engineer, you will operate at the forefront of advanced machine learning research and dependable engineering practices. Your role will involve designing and executing experiments on large language models, developing the necessary tools for extensive model training and evaluation, and translating findings into publishable research. You will work collaboratively with CAIS researchers and external academic and commercial partners, utilizing our compute cluster to conduct large-scale training and evaluations. Your work will focus on critical areas such as AI honesty, robustness, transparency, and the identification of trojan/backdoor behaviors, all aimed at mitigating real-world risks posed by sophisticated AI systems.

Join the Center for AI Safety (CAIS), a premier research and advocacy organization dedicated to addressing the critical challenges posed by artificial intelligence. Our mission is to mitigate societal-scale risks associated with AI, leveraging technical research, field-building initiatives, and policy engagement. Together with our sister organization, the Center for AI Safety Action Fund, we are at the forefront of AI safety.As the Special Projects Manager, you will collaborate closely with the Executive Director and Managing Director to identify, assess, and initiate innovative projects within CAIS's early-stage pipeline. You will manage the entire project lifecycle—from exploring opportunities and drafting concise project briefs to creating timelines and budgets, while ensuring timely delivery through our project management system. This dynamic role involves coordinating with researchers, vendors, and other stakeholders, managing risks, and providing clear documentation and reporting to leadership. You will be instrumental in transforming ambitious AI safety concepts into tangible outcomes with agility, precision, and an unwavering focus on our mission.AI safety communication is experiencing significant momentum, with our videos, articles, and social media posts reaching millions of viewers. CAIS is poised to capitalize on this wave of public interest by launching several impactful projects on an accelerated timeline.

The Center for AI Safety (CAIS) is at the forefront of research and advocacy dedicated to addressing the societal-scale challenges posed by artificial intelligence. Our mission is to mitigate the risks associated with AI through innovative technical research, initiatives to foster the field, and strategic policy engagement. Together with our sister organization, the Center for AI Safety Action Fund, we tackle some of the most pressing issues in AI today. In the role of Senior Research Engineer, you will immerse yourself in the dynamic intersection of pioneering machine learning research and dependable engineering practices. You will own research projects from inception to publication, working autonomously with guidance from an advisor. Your responsibilities include designing and conducting experiments on large language models, developing the necessary tools for large-scale model training and evaluation, and transforming findings into research publications. You will collaborate closely with CAIS researchers, as well as external academic and commercial partners, utilizing our compute cluster for extensive training and evaluation. Your work will cover critical areas such as AI honesty, robustness, transparency, and the investigation of trojan/backdoor behaviors, all aimed at reducing the real-world risks posed by advanced AI systems.

Join aisafety as a Program Manager, where you will lead innovative projects aimed at enhancing safety protocols across various industries. You will work closely with cross-functional teams to ensure the successful execution of programs that align with our mission of promoting safety and efficiency.

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

Join aisafety as a Research Engineer Intern for the Fall 2026 term! This internship is an excellent opportunity to gain hands-on experience in the field of safety engineering, where you will collaborate with seasoned professionals on innovative projects. You will be involved in research and development activities, contributing to advancements in safety solutions.

The Center for AI Safety (CAIS) is a pioneering research and advocacy organization dedicated to addressing and mitigating the significant risks posed by artificial intelligence on a societal scale. Our team tackles the most pressing challenges of AI through a combination of technical research, initiatives to build the field, and active policy engagement, in collaboration with our affiliated entity, the Center for AI Safety Action Fund (CAISAF).If you are passionate about advancing our mission but do not see a current job opening that aligns with your skills and experiences, we encourage you to submit an expression of interest. This opportunity allows you to present your unique skills, background, and innovative ideas on how you can contribute to our work at CAIS. We continuously review these submissions against our evolving needs and may reach out if we identify a strong match.If you are committed to reducing catastrophic risks associated with AI and have valuable insights or expertise to offer, 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.

About CartesiaAt Cartesia, our vision is to create the future of artificial intelligence—intelligent systems that are seamlessly integrated into daily life. We aim to overcome current limitations by enabling models to continuously understand and analyze vast streams of audio, video, and text data—ranging from 1 billion text tokens to 1 trillion video tokens—right on your device.Our pioneering team, comprised of PhDs from the Stanford AI Lab, has developed State Space Models (SSMs), a groundbreaking approach to training efficient, large-scale foundation models. With a rich blend of expertise in model innovation and systems engineering, alongside a product-focused engineering team, we are committed to developing and delivering cutting-edge AI models and user experiences.Supported by prominent investors including Index Ventures and Lightspeed Venture Partners, as well as many esteemed advisors and over 90 angel investors from diverse industries, we are at the forefront of AI advancements.About The RoleIn our quest to create truly global AI, we must train our models using datasets that represent the vast diversity of languages and cultures around the world. We are looking for a Research Engineer to take charge of the quality and comprehensiveness of the data that drives our models. As our in-house expert in global data, you will ensure that our models excel across multiple languages, leveraging your keen understanding of linguistic subtleties and your enthusiasm for building inclusive, large-scale datasets.Your ImpactDesign and construct extensive datasets for model training, conducting controlled experiments to evaluate their effect on model performance.Develop assessments for speech models through both manual annotation and automated evaluation metrics.Utilize data generation techniques to enhance model intelligence and reduce biases.Create automated quality control systems to validate and filter the generated data.Collaborate with product teams to ensure optimal support for key languages and markets.What You BringProven experience in developing or working with extensive multilingual datasets.Familiarity with generative models, including speech, text, or multimodal systems.Ability to guide human annotation and evaluation across various languages.Strong analytical skills and a passion for data-driven decision-making.

About MercorMercor sits at the forefront of labor markets and artificial intelligence research, collaborating with premier AI laboratories and enterprises to harness the human intelligence crucial for AI evolution.Our expansive talent network empowers the training of cutting-edge AI models, akin to how educators impart knowledge to students—sharing insights, experiences, and contexts that transcend mere code. Currently, our network comprises over 30,000 experts, generating collective earnings exceeding $2 million daily.At Mercor, we are pioneering a unique category of work where expertise fuels AI progress. Realizing this vision necessitates a bold, fast-paced, and deeply dedicated team. You will collaborate with researchers, operators, and AI firms that are at the vanguard of transforming systems that redefine society.As a profitable Series C company, Mercor is valued at $10 billion and maintains an in-office presence five days a week at our new headquarters in San Francisco.About the RoleIn your capacity as a Research Engineer at Mercor, you will operate at the intersection of engineering and applied AI research. You will play a pivotal role in post-training and Reinforcement Learning from Human Feedback (RLVR), synthetic data generation, and large-scale evaluation workflows essential for advancing frontier language models.Your contributions will help train large language models to adeptly utilize tools, exhibit agentic behavior, and engage in real-world reasoning within production environments. You will be instrumental in shaping rewards, conducting post-training experiments, and constructing scalable systems to enhance model performance. Your responsibilities will also include designing and evaluating datasets, creating scalable data augmentation pipelines, and developing rubrics and evaluators that expand the learning potential of LLMs.

HUD builds infrastructure for generating and evaluating reinforcement learning (RL) training data for advanced AI agents. The team is also developing a marketplace to connect leading labs with high-quality training data. HUD's platform serves frontier labs, Fortune 500 companies, and startups. The company is backed by $15M in funding from top venture capital firms and is part of Y Combinator's W25 cohort. Role overview HUD is seeking Research Engineers in San Francisco to help strengthen quality assurance for training data produced by partner organizations. This position centers on building systems that maintain and improve data quality as demand increases. What you will do Set and uphold quality standards for training datasets. Develop tools and workflows for auditing datasets from suppliers, including sampling methods, validation pipelines (using rules and models), and feedback systems. Assess and refine human-in-the-loop review processes to support quality assurance. Collaborate with data vendors to resolve quality issues, share insights, and encourage better data generation practices. Integrate QA findings into internal tools and the data vendor portal to reduce anomalies, inconsistencies, and edge cases. Requirements Strong skills in Python, Docker, and Linux environments. Experience working with large datasets. Ability to learn quickly and adapt in technical contexts, such as programming competitions. Background in early-stage tech startups and ability to work independently. Familiarity with modern AI tools and large language models (LLMs). Clear communication skills for collaborating remotely across time zones. Preferred qualifications Understanding of common issues in training data. Background in building data validation pipelines or human-in-the-loop review systems. Strong attention to detail, with the ability to identify subtle data inconsistencies or edge cases. Experience designing metrics, experiments, and QA processes, not just executing them.

The Center for AI Safety (CAIS) stands at the forefront of research and advocacy, dedicated to addressing the pressing societal-scale risks posed by artificial intelligence. Our mission is to confront AI's greatest challenges through rigorous technical research, innovative field-building initiatives, and impactful policy engagement, in collaboration with our sister organization, the Center for AI Safety Action Fund.We are committed to maximizing our positive impact through an expansive array of programs. Notable achievements include the introduction of the most widely utilized AI capabilities measurement embraced by leading AI companies, operating a substantial compute cluster for AI safety research that has been cited over 16,000 times, and the publication of a global statement on AI Risk, endorsed by prominent figures including Geoffrey Hinton, Yoshua Bengio, and leading AI CEOs.We seek enthusiastic and proactive individuals to manage and implement programs spanning public engagement, operations, publications, special projects, and research. Potential projects may involve collaborating with the creators of #TeamTrees to launch a campaign focused on AGI, aiding researchers in developing benchmarks related to deception and weaponization risks, establishing an AI safety hub in Washington, D.C., or devising strategies to engage YouTube creators and long-form content producers on AI safety topics. At CAIS, we are a fast-paced, meritocratic organization where responsibilities and leadership opportunities expand for those who demonstrate initiative and consistently deliver results.

Product ResearcherAs a Product Researcher at Firecrawl, you will be pivotal in shaping our future product development. Our engineering team is currently focused on delivering robust infrastructure, while our sales and support teams effectively manage incoming queries. However, we are seeking a dedicated individual to drive our larger product initiatives—those that will elevate our tool into a leading platform. Currently, we possess a cutting-edge commercial research paper search endpoint with limited use. Our features, including answers, reranking, and monitoring, require the dedicated focus of a researcher like you. You will ensure that we prioritize effectively based on genuine customer needs.This role transcends traditional product management; you will immerse yourself in the field, engaging in frequent conversations with customers—conducting 15-25 interactions weekly. These will include discovery calls, user interviews, onboarding sessions, and discussions surrounding churn. Your mission is to uncover insights that reveal our customers' challenges and aspirations, effectively bridging the gap between our developers' efforts and our product roadmap.

At magic.dev, we are committed to advancing humanity by developing safe artificial general intelligence (AGI) that tackles the world's most pressing challenges. Our unique approach focuses on automating research and code generation to enhance model performance and alignment more effectively than traditional methods. By leveraging cutting-edge pre-training, domain-specific reinforcement learning, ultra-long context processing, and efficient inference-time computation, we aim to redefine the capabilities of AGI.Role OverviewAs a Research Engineer, you will play a pivotal role in training, evaluating, and deploying large-scale AI models alongside innovative inference-time computing methods. You will contribute to the creation of extensive internet-scale datasets and support the prototyping of groundbreaking research and product initiatives.Key ResponsibilitiesEnhance inference throughput for cutting-edge model architecturesDevelop and refine frameworks that underpin our research and production processesTrain trillion-parameter models using large GPU clustersCurate post-training datasets to bolster specific capabilitiesConstruct internet-scale data pipelines and web crawlersDesign, prototype, and optimize innovative model architecturesContribute to cutting-edge research in long-context, inference-time computation, reinforcement learning, and additional domainsQualificationsProven software engineering expertiseIn-depth understanding of deep learning literatureExperience with both pre-training and post-training of large language models (LLMs)Strong capability to generate and assess research ideasFamiliarity with large distributed systemsProficient in managing substantial ETL workloadsCompensation and BenefitsAnnual salary ranging from $225,000 to $550,000 based on experienceEquity is a significant component of total compensation401(k) plan with a 6% salary matchComprehensive health, dental, and vision insurance for you and your dependentsUnlimited paid time offVisa sponsorship and relocation assistance availableBe part of a small, dynamic, and focused team

About Resolve AIAt Resolve AI, we are redefining the role of software maintenance and production troubleshooting by creating a revolutionary, fully autonomous AI Production Engineer. Our technology is designed to diagnose and resolve intricate system issues from start to finish.Founded by industry leaders Spiros Xanthos and Mayank Agarwal, who are the masterminds behind OpenTelemetry and have previously spearheaded initiatives at Splunk Observability, our team boasts two successful exits to Splunk and VMware.Having successfully secured over $150M in funding from prestigious investors like Lightspeed, Greylock, and Unusual Ventures, alongside notable individuals such as Jeff Dean (Chief Scientist, Google DeepMind) and Fei-Fei Li (Professor, Stanford), we are well-positioned for growth.Joining Resolve AI now presents a unique opportunity to be part of an AI-driven company that is at the forefront of transforming engineering workflows.

About Liquid LabsAt Liquid AI, research has always been at the forefront of our mission. Liquid Labs serves as a dedicated internal research accelerator, facilitating groundbreaking advancements in the development of intelligent, personalized, and adaptive machines.Our roots extend back to MIT CSAIL, where pioneering work on Liquid Neural Networks established a new category of efficient sequence-processing architectures. This research laid the groundwork for our Liquid Foundation Models (LFMs), which are scalable, multimodal models designed for real-world applications in resource-constrained settings.In Liquid Labs, we continue this legacy by advancing the realm of efficient, adaptive intelligence through both fundamental research and practical engineering efforts.We collaborate closely with Liquid’s core foundation model and systems teams to turn theoretical concepts into deployable capabilities, setting the stage for a new era of powerful and efficient intelligent systems.About The Role:As a Research Engineer at Liquid Labs, you will be part of a dynamic, high-impact team pushing the boundaries of adaptive intelligence. You will be responsible for designing and implementing innovative architectures, training methodologies, and inference strategies to expand the potential of efficient AI.Your work will blend research and engineering, as you translate scientific concepts into functional systems, publish findings that advance the field, and deploy solutions that redefine what is achievable.While we prefer candidates from San Francisco and Boston, we welcome applications from other locations within the United States.

About Handshake Handshake connects over 20 million knowledge workers with 1,600 educational institutions and 1 million employers, including every Fortune 50 company. The platform supports career growth and upskilling by bridging students, educators, and employers. Handshake has seen rapid expansion, tripling its Annual Recurring Revenue (ARR) by 2025. Why Work at Handshake? Shape the future of careers in the AI economy at a global level. Collaborate with top AI labs, Fortune 500 companies, and leading educational institutions. Work alongside experienced professionals from organizations such as Scale AI, Meta, xAI, Notion, Coinbase, and Palantir. Contribute to a company with significant growth and revenue potential. Role Overview: AI Research Engineer Design and build advanced post-training systems in partnership with research scientists and domain experts. Develop and maintain infrastructure for large-scale model training and specialized data processing. Create frameworks to verify the quality and integrity of domain-specific datasets. Develop benchmarks for large language models to improve evaluation and capability assessments. Optimize software and hardware performance to accelerate post-training experiments and deployment. Work across teams to ensure thorough validation of model improvements. Location San Francisco, CA

Job DescriptionEmbrace the future of competitive advantage with Eragon, where we create bespoke AI systems that are meticulously tailored to understand your unique business landscape.At Eragon, we focus on developing AI models that leverage proprietary data, deployed directly within customer environments and continuously refined through real-world interactions. Our models not only respond but evolve, improving with each user engagement.We utilize a cutting-edge reinforcement learning framework known as RLQF (Reinforcement Learning from Query Feedback) that transforms user interactions into valuable training signals, establishing a cycle of ongoing enhancement that surpasses traditional fine-tuning methods.The RoleAs an Applied Research Engineer, you will be responsible for designing, training, and deploying advanced models that drive real business operations.This position is not about theoretical research; you will engage directly with customer data, constraints, and feedback, crafting solutions that excel in production settings. You will manage the entire lifecycle of the project, from defining the problem and designing data structures to training, evaluating, and iterating based on live performance.What You’ll DoTrain and adapt models: Fine-tune and post-train models on customer-specific data utilizing RLQF among other techniques.Close the loop: Convert real user interactions, corrections, and workflows into actionable training signals.Own end-to-end systems: Oversee the process from data ingestion and curation through to training, evaluation, and deployment.Evaluate in production: Create evaluation frameworks that accurately reflect real-world performance, rather than relying solely on benchmarks.Work with customers: Collaborate closely with users to comprehend their workflows and translate these into model functionalities.Ship and iterate: Focus on the continuous improvement of models based on live feedback and measurable outcomes.What We’re Looking ForExtensive hands-on experience in training, fine-tuning, or post-training machine learning models.Proficiency in handling messy, real-world data as opposed to only clean benchmarks.Familiarity with reinforcement learning techniques, feedback-driven training such as RLHF or RLAIF, and evaluation systems.Adeptness at quickly transitioning from problem identification to data management, model development, and iterative improvement.Strong engineering instincts with a comfort level in managing systems end-to-end.A proactive approach to shipping and enhancing systems, rather than solely focusing on research.

At Thinking Machines Lab, our mission is to empower humanity by advancing collaborative general intelligence. We aspire to create a future where everyone can access the knowledge and tools necessary to harness AI for their individual needs and aspirations.Our team consists of scientists, engineers, and innovators who have developed some of the most renowned AI products, including ChatGPT and Character.ai, as well as open-weight models such as Mistral. We are also contributors to popular open-source initiatives like PyTorch, OpenAI Gym, Fairseq, and Segment Anything.About the RoleWe are seeking talented engineers to develop the libraries and tools that will expedite research at Thinking Machines. You will take charge of our internal infrastructure, which includes evaluation libraries, reinforcement learning training libraries, and experiment tracking platforms, all aimed at enhancing research velocity over time.This position emphasizes collaboration; you will engage directly with researchers to pinpoint bottlenecks and challenges. Your success will be measured by the trust researchers place in your systems and their enjoyment of using them.What You'll DoDesign, develop, and manage research infrastructure, including evaluation frameworks, RL training systems, experiment tracking platforms, visualization tools, and shared utilities.Create high-throughput, scalable pipelines for distributed evaluation, reward modeling, and multimodal assessments.Establish systems for reproducibility, traceability, and stringent quality control throughout research experiments and model training processes. Implement monitoring and observability.Collaborate closely with researchers to identify obstacles and unlock new capabilities. Manage research tools like a product manager, actively seeking feedback and tracking user adoption.Work alongside infrastructure, data, and product teams to ensure seamless integration of tools across the technical stack.

Join xdof at a pivotal moment as we lead the charge in the development of general-purpose robotics. With frontier labs racing to create advanced robotic systems, high-quality training data is a critical challenge. Our mission is to build the essential infrastructure that supports foundational models – from data collection systems and operational capabilities to an exabyte-scale data warehouse and innovative software toolchains. This will empower our partners to advance the field of robotics.As a Research Engineer, you will be at the forefront of designing, constructing, and deploying real-world robotic learning systems. Your work will encompass manipulation, locomotion, and control, transitioning robots from raw hardware to fully operational systems.This hands-on position requires you to take ownership of systems from inception to deployment on actual robots. You will play a crucial role in establishing the technical foundations that facilitate large-scale robotic learning.