Member of Technical Staff, Enterprise AI, Remote- Full-time

Job Title: Member of Technical Staff, Enterprise AI Job Type: Full-time Location: Remote Required Skills: Research Signal Judgment ML-Oriented Data Design Ops-to-Research Translation Enterprise AI The Role: As a Member of Technical Staff, you will function as a forward-deployed research partner embedded directly within enterprise AI systems. You will work on live workflows, uncover real-world failure modes, and drive rapid experimental cycles to improve system performance. What You’ll Do: Embed within enterprise AI workflows as a research collaborator, working alongside domain experts and client teams. Surface, formalize, and prioritize system failure modes in real-world deployments. Design high-signal datasets and evaluation protocols to target identified weaknesses. Run tight experimental loops to validate hypotheses and quantify improvements. Produce clear, decision-oriented analyses of system behavior and performance. Develop and benchmark agentic workflows, focusing on robustness and scalability. Build lightweight tooling to support evaluation, data curation, and rapid iteration. Contribute to internal and external research artifacts, including reports and benchmarks. Who You Are: Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field. Strong judgment for research signal quality, including data selection and evaluation design. Experience designing datasets and evaluation frameworks for ML systems. Ability to translate ambiguous operational issues into structured research problems. Familiarity with RL environments and/or agentic system evaluation. Clear, concise communicator with a bias toward actionable insight. Proven ability to execute in fast iteration cycles and high-ambiguity settings. Collaborative mindset with experience working across research, product, and domain teams. Preferred: Strong client-facing experience, particularly in technical or research-driven environments. Experience building internal research or evaluation tooling. Contributions to benchmarks, research publications, or open research initiatives. Exposure to enterprise AI deployments or forward-deployed research models. Originally posted on Himalayas