Staff Research Engineer/Scientist

Join our Core AI Research team as a Staff Research Engineer/Scientist. In this role, you will lead a major workstream in agent learning and recursive self-improvement, designing and executing end-to-end research projects that improve long-horizon enterprise agents. You will also research model post-training methods, build improvement flywheels, create realistic training environments, run rigorous experiments, and develop capabilities across multiple modalities. Additionally, you will partner with other researchers, engineering, and product teams to move validated methods into reliable enterprise systems.

• Independently lead a major workstream in agent learning and recursive self-improvement, turning systematic failures and successful trajectories into hypotheses, experiments, and deployable improvements.
• Design and execute end-to-end research projects that improve long-horizon enterprise agents across various capabilities, including planning, reasoning, memory, tool use, and multi-agent coordination.
• Build improvement flywheels that mine trajectories and production-safe signals, identify recurring failure modes, generate or curate data, propose interventions, and measure generalization before promotion.

• Strong software engineering fundamentals and the ability to build reliable, testable, reproducible systems in collaboration with AI/ML infra and software engineers
• 7+ years of relevant ML/AI research or engineering experience, or equivalent evidence of research depth and impact. A PhD or other advanced degree is required
• Experience with distributed training, rollout, and/or inference using some subset of PyTorch distributed/FSDP, DeepSpeed or Megatron; verl, TRL, OpenRLHF or comparable post-training systems; and vLLM, SGLang, or comparable inference stacks
• Publications at top-tier venues (ICLR, NeurIPS, ICML, ACL, EMNLP, AAAI)
• Strong foundations in machine learning, deep learning, reinforcement learning and/or probabilistic/statistical experimentation, with hands-on experience training or adapting large language models or multi-modal models
• Experience designing decision-useful evaluations, including strong dataset partitions, trajectory analysis, graders/verifiers, error taxonomies, and robust conclusions under noisy measurements
• Experience in leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving. This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI's potential impact on the function or industry
• Advanced Python and PyTorch skills, including the ability to modify training loops, model code, data pipelines, evaluators, or research infrastructure
• Practical depth in agentic AI: tool use, environments, planning/reasoning, memory/context, retrieval, long-horizon execution, or multi-agent systems
• A track record of independently taking ambiguous research questions from hypothesis through implementation, experiment, analysis, and measurable outcome
• Research and production experience in multimodal, document AI, computer vision, speech/audio, multilingual, or cross-lingual modeling
• Experience with enterprise agents, stateful workflows, browser/computer use, MCP or similar tool protocols, simulation environments
• Experience with synthetic data, model-generated feedback, proposer/critic/verifier systems, automated experimentation, or evolutionary/search-based optimization
• Experience operating GPU clusters and experiment platforms using Ray, Kubernetes, Slurm, cloud accelerators, or comparable systems
• Evidence of research impact through shipped capabilities, high-quality publications, patents, benchmarks, or substantive open-source contributions