Research Engineer

• Apply modern software development practices throughout the entire software development lifecycle
• Create large scale data processing pipelines for machine learning algorithms
• Work collaboratively in a team-oriented environment
• Deliver timely solutions with a strong sense of urgency
• Contribute innovative ideas and solutions, accountable for end-to-end deliveries
• Engage and communicate effectively with cross-functional partners

Senior Research Engineer

• Work alongside other motivated and talented research engineers to bring cutting-edge theory into customer-facing practice
• Build tools and pipelines to support ML and AI efforts
• Design, build, deploy, and maintain production services, like APIs, UIs, agentic evaluators, and more
• Roll out scalable cloud architectures
• Help with end-user requirements gathering and executive presentations

Applied Research Engineer

• Design and run new evaluations of Claude's capabilities — reasoning, agentic behavior, knowledge, safety properties — and produce visualizations that make the results legible to researchers and decision-makers
• Build and harden the distributed eval execution platform so hundreds of evals run reliably against checkpoints throughout production RL training runs
• Own the dashboards researchers and leadership use to monitor model health during training, improving signal-to-noise, reducing latency, and making regressions impossible to miss
• Debug anomalous eval results mid-training-run, determine whether the cause is a model change or an infrastructure issue, and communicate the answer clearly under time pressure
• Improve the tooling, libraries, and workflows researchers use to implement and iterate on evaluations
• Partner with research teams across the full lifecycle of a new capability — from defining what to measure to interpreting results as training progresses
• Run experiments to characterize how prompting, sampling, and scaffolding choices affect results on internal and industry benchmarks
• Communicate evaluations and their results to internal stakeholders and, where appropriate, external audiences

Research Engineer, Model Evaluations

• Design, prototype, and implement agentic AI systems that perform reliably across complex enterprise workflows
• Build compound AI architectures that combine planning, tool use, retrieval, memory, evaluation, orchestration, and execution
• Investigate how agents reason, coordinate, recover from errors, and interact with external systems under real-world constraints
• Develop evaluation frameworks that measure agent behavior, task completion, reliability, robustness, and failure modes
• Create tools and abstractions that make agent behavior easier to observe, debug, test, and improve
• Partner with AI Researchers to explore new agent architectures and with AI Engineers to harden successful approaches for production use
• Integrate agents into customer APIs, applications, data platforms, and operational workflows
• Communicate clearly with internal teams and customer stakeholders about agent capabilities, limitations, tradeoffs, and risks

• Design structured experiments with real controls and statistical rigor
• Build regression test coverage to know when a global prompt change is breaking things across workflows
• Create a pipeline of validated, high-confidence improvements for engineering
• Work with the team to prioritize what gets shipped based on experiment results
• Own prompt design across Mia's agent workflows
• Stay current on prompting research, bring back ideas worth testing
• Prototype new approaches locally
• Collaborate with Product and Engineering teams
• Translate experiment results into clear recommendations
• Build out evaluation templates and frameworks

Research Engineer jobs in New York

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Senior Research Engineer

Applied Research Engineer

Research Engineer, Model Evaluations

Research Engineer

AI Research Engineer