AI Research Scientist Jobs

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The AI Researcher will work across the model development loop including designing and testing architecture changes and training regimes for large language models, running controlled experiments at scale to isolate causal effects, studying failure modes in reasoning, generalisation, robustness, and representation, shaping objectives, data mixtures, and optimisation choices that influence model behaviour, building and refining evaluations that measure capability and reliability, analysing training dynamics using logs, metrics, and model outputs, collaborating with ML systems engineers on distributed training and training operations, and writing clear internal notes to translate experimental results into design decisions. The role requires substantial time spent in code, training runs, logs, and evaluation outputs aiming for clarity about what improves the model and why.

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The job responsibilities include having industry experience as a research engineer in an AI-related company and being excited to work, learn, and teach within a collaborative team on challenging problems.

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The member of the technical staff will be responsible for collaborating with Modal’s SDK team and other product engineers to build out a framework and process for agent productivity evaluation. They will treat developer experience optimization as a scientific problem by defining quantitative objectives, designing systems to measure performance, and translating results into product improvements. They are also expected to stay on top of new developments in tools and workflows, and to work with customers to understand how they are using coding agents with Modal and where additional value can be provided.

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The Research Scientist will design and conduct evaluations of Abridge models and products, engage with external researchers and other stakeholders on research related to ambient AI and Abridge data, develop a user-centric and patient-centric mindset to ground research in the real-world experience of providers and patients, collaborate with cross-functional product teams to align research with current practices and product roadmap, write technical reports and present findings to internal and external stakeholders, contribute to the research community by publishing original research, and mentor research interns.

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As a Senior AI Researcher for reinforcement learning, you will shape and improve the underlying RL methodology, maintain a high-quality training code-base, and conduct large-scale experiments to hill-climb performance benchmarks. You will conduct large-scale LLM training runs, analyze evaluation scores in depth, propose hypotheses for improvement, and directly implement them to maximize performance on benchmarks. You will identify, implement, and iterate on novel approaches to multi-turn reinforcement learning, optimize RL training loops for large-scale training by identifying bottlenecks, and collaborate cross-functionally to turn raw feedback into actionable training signals to ensure RL iterations lead to measurable improvements in downstream performance.

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Conduct original research to advance the state of the art in machine learning and artificial intelligence. Design, implement, and evaluate novel algorithms, models, or training approaches at large scale. Collaborate with researchers and engineers to translate research insights into production systems and real-world applications.

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The role involves tackling complex problems end-to-end and owning a part of the product with decision-making responsibilities spanning the LLM pipeline, infrastructure, backend, and UX. The work includes pushing the most advanced AI models to their limits, building a product that changes how companies make decisions, and contributing to technical challenges such as developing an AI-powered research agent, building customer preference models and synthetic personas, creating a database of millions of humans to improve user targeting, enhancing realtime video interviews with emotional understanding, and developing a distributed information mining agent that finds the right people to answer questions and provides actionable recommendations. The role requires maintaining high quality output, communicating tradeoffs, problems, and blockers clearly and directly, and working with minimal meetings while owning responsibilities within a startup environment.

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As an ML Research Scientist at Eight Sleep, responsibilities include using AI and Machine Learning to transform sensor data into personalized intelligent health and fitness experiences. You will work closely with a cross-functional R&D and production team to prototype and ship solutions that improve sleep and health. Specific projects involve advancing the Pod's adaptive thermoregulation system using reinforcement learning and closed-loop control, developing multimodal health foundation models integrating physiology and environmental context from various data sources, and building high-fidelity physiological simulators to model the impact of daily behaviors on sleep and readiness. The role requires applying machine learning techniques to health-related problems and data to deliver impactful products for users.

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This role leads the Automated Red Teaming (ART) effort, building scalable, research-driven systems that continuously discover failure modes in the models and mitigations, and translate those findings into actionable, production-facing improvements aimed at maximizing counterfactual reduction in expected harm by identifying high-leverage, least-covered weaknesses early and reliably. The researcher will own the research and technical direction for automated red teaming across catastrophic risk areas, initially focusing on automated classifier jailbreak discovery (cyber and bio), automated bio threat-development elicitation (worst-feasible planning uplift), and chain-of-thought monitoring evasion probing and related loss-of-control evaluations. The person in this role will partner closely with vertical risk teams (Cyber, Bio, Loss of Control) to define threat models, prioritize targets, and implement mitigations; with the Classifiers team to convert discovered attacks into training data, evaluations, and measurable robustness improvements; and with product, engineering, and safety stakeholders to ensure ART outputs are operationally useful, not just theoretically interesting.

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Design, evaluate, and productionize next generation AI inference systems by researching, implementing, and evaluating state-of-the-art techniques including speculative decoding, prefill–decode disaggregation, quantization, and kernel-level optimizations focused on real-world customer use cases. Design and run experiments to understand trade-offs in latency, throughput, cost, and quality to guide system and model design decisions. Build and iterate on high-performance inference prototypes by translating research ideas into practical implementations, optimizing performance-critical kernels, and improving execution efficiency on modern accelerators. Analyze real-world inference workloads to identify opportunities for efficiency and scalability improvements, stay current with advances in ML systems and inference research, and share findings through internal reports and external contributions. Collaborate closely with systems engineers, ML engineers, and infrastructure teams to drive research ideas toward impactful applications in real-world environments, and define and contribute to the company roadmap impacting product and customers.