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(Enterprise, AI) Member of Technical Staff
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(Enterprise, AI) Member of Technical Staff
Member of Technical Staff (Enterprise AI)
Location: Remote Type: Full-time
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 behaviour and performance
- Develop and benchmark agentic workflows, focusing on robustness and scalability
- Build lightweight tooling to support:
- Evaluation
- Data curation
- Rapid iteration
- Contribute to internal and external research artifacts, including reports and benchmarks
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Who You Are
Requirements
- Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field
- Strong ability to determine research signal quality, including:
- Data selection
- 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
- Agentic system evaluation
- Clear, concise communicator with a bias toward actionable insights
- Proven track record of executing in:
- Fast iteration cycles
- High-ambiguity settings
- Collaborative mindset with experience working across research, product, and domain teams
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Preferred
- Strong client-facing experience, particularly in technical or research-driven environments
- Experience building internal research or evaluation tooling
- Contributions to:
- Benchmarks
- Research publications
- Open research initiatives
- Exposure to:
- Enterprise AI deployments
- Forward-deployed research models
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