THE ROLEYou'll turn difficult problems in real workflows into research questions, develop improvements across models and agent systems, and carry results into production. This role is for a researcher with strong engineering ability who can move between model training, experimentation, and applied AI systems, choosing the right approach for our users.
WHAT WE ARE LOOKING FOR- Deep ML foundations: You understand learning, optimization, probability, and statistics, and use that understanding to reason about model behavior and experimental results.
- Hands-on model experience: You have trained, adapted, or improved models and can explain the decisions behind your work. You bring depth in areas such as post-training, reinforcement learning, etc.
- Research judgment: You turn ambiguous problems into clear hypotheses, design experiments, and can develop new approaches.
- Strong applied judgment: You stay close to real workflows and know when to improve the model, the data, or the system around it. You weigh quality, reliability, latency, and cost.
- High agency and ownership: You help define the research direction, take responsibility for outcomes, and move from investigation to execution without waiting for a detailed plan.
- Clear collaborators: You communicate findings and uncertainty clearly, seek context from domain experts, and help research, engineering, and product make decisions.
WHAT YOU'LL DO- Own applied research projects from problem definition and experiment design through implementation and production validation.
- Train and adapt models for the reasoning, retrieval, and decision-making tasks
- Develop and test improvements to agent systems, including tool use, context, memory, planning, and recovery from failures.
- Build datasets, evaluation environments, and grading methods that capture the difficulty of real workflows.
- Inspect model outputs and agent traces, identify recurring failure modes, and turn those findings into changes to training, data, or system design.
- Build the experimentation tools and pipelines needed to run, compare, and reproduce research efficiently.
- Partner with AI Strategy, product, and engineering to understand workflows and ship improvements to production.
NICE TO HAVE- You have carried a research idea into a product or system used in real workflows.
- You have research publications, open-source contributions, or substantial industry work in ML, LLMs, agents, or evaluation.
- You have experience with reward modeling, synthetic data, or learning from human feedback.
- You have worked with distributed training, inference optimization, or large-scale experimentation.
- You have worked in a high-growth early-stage company.
BENEFITS- Competitive compensation, including meaningful equity.
- Medical, dental, and vision insurance for employees and dependents.
- Generous PTO policy.
- Paid parental leave.
