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(Enterprise, AI) Member of Technical Staff
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Member of Technical Staff (Enterprise AI)
Job Type: Full-time Location: Remote
About the Role
As a Member of Technical Staff, you will act as a forward-deployed research partner embedded directly within enterprise AI systems. Your role involves tackling live workflows, identifying real-world failure points, and accelerating experimental improvements to enhance system reliability and effectiveness.
What You’ll Do
- Embed within enterprise AI workflows as a research collaborator, collaborating closely with domain experts and client teams.
- Identify, formally document, and prioritise system failure modes in real-world deployments.
- Design high-impact datasets and evaluation protocols to address identified weaknesses.
- Run iterative experiments to test hypotheses and quantify performance gains.
- Produce actionable, decision-oriented analyses on system behaviour and performance metrics.
- Develop and benchmark agentic workflows, focusing on robustness and scalability.
- Build lightweight evaluation tooling to support data curation, experimentation, and iterative improvements.
- Contribute to internal/external research artifacts, including reports, benchmarks, and publications.
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Who You Are
Required
- A Master’s degree in Computer Science, Machine Learning, AI, or a related technical discipline.
- Strong ability to assess research signal quality, including criteria for dataset selection and evaluation design.
- Experience designing datasets and evaluation frameworks for ML systems.
- Skill in translating operational challenges into structured research questions.
- Familiarity with RL environments and/or agentic system evaluation.
- Clear, concise communicator with a focus on delivering actionable insights.
- Proven ability to work efficiently under fast-paced iterations and high ambiguity.
- Experience collaborating across research, product, and domain teams.
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Preferred
- Strong client-facing experience, particularly in technical or research-rich environments.
- Background in building internal research or evaluation tooling.
- Contributions to public benchmarks, research publications, or open research initiatives.
- Exposure to enterprise AI deployments or forward-deployed research models.
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