Forward Deployed Machine Learning Engineer
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Job Description
Forward Deployed ML Engineer building benchmarks, model evaluations, and backend infrastructure for Protege’s secure AI training-data exchange platform. Partnering with researchers, customers, and DataLab to turn evaluation engagements into products.
Responsibilities:
- Partner with the GM and early customers to define strong evaluations across domains
- Collaborate with researchers to design and build benchmarks
- Establish standards for processing different modalities
- Build backend infrastructure including data pipelines, execution environments, storage, and orchestration
- Stand up sandboxed environments for agentic evaluations requiring tools, code execution, or multi-step tasks
- Identify repeatable evaluation patterns, infrastructure gaps, and product opportunities from live engagements
- Partner with DataLab on domain-specific data and research questions
- Build understanding of the evaluation landscape, strategy, customer demand, and platform/data partner capabilities
- Identify major technical bets and ship multiple iterations of evaluation infrastructure
- Own the engineering portion of customer engagements end to end
Requirements:
- 4+ years of engineering experience
- Hands-on machine learning work evaluating models
- Previous ownership of backend and infrastructure
- High ambiguity tolerance and bias to action
- Comfort working with urgency to meet market demands
- Strong written communication
- Prior experience building benchmarks, evaluations, or human data pipelines for LLMs (nice to have)
- Experience at a frontier lab, evaluation-focused team, or research organization (nice to have)
- Founding or early engineer experience at a fast-moving startup (nice to have)
- Familiarity with agentic systems, RL environments, code-execution sandboxes, and TEE/TREs (nice to have)
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