Job Description
Job Title: Member of Technical Staff (Enterprise AI)
Job Type: Full-time
Location: Remote
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.