Job Description

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.