Forward Deployed Machine Learning Engineer

📋 Job Overview

Protege is building a platform to solve the biggest unmet need in AI — getting access to the right training data. The process today is time intensive, incredibly expensive, and often ends in failure. The Protege platform facilitates the secure, efficient, and privacy-centric exchange of AI training data. We're hiring a Forward Deployed Machine Learning Engineer in our Benchmarks and Evaluations vertical. You'll be the first MLE dedicated to this vertical and will work directly with the GM and our researchers to scale Protege’s position as a renowned leader in the space.

🏢 About Protege

We’re a lean, fast-moving, high-trust team of builders who are obsessed with velocity and impact. Our culture is built for people who thrive on ambiguity, own outcomes, and want to shape the future of data and AI. Solving AI’s data problem is a generational opportunity. We’re backed by world-class investors and already powering partnerships with some of the most ambitious teams in AI.

🎯 The Role

As an early engineer in the Benchmarks and Evaluations vertical, this role is an opportunity to help build the technical foundation for a critical area that greatly benefits current and future customers. At Protege, we believe that real world data is one of the largest bottlenecks to AI progress. Our data and data expertise position us to be neutral arbiters for the market, helping model builders understand the current performance of their models, identify what data will improve performance, and show that improvement over time. Benchmarks and evaluations power that cycle.

✅ Key Responsibilities

• Work on the eval foundation by partnering with the GM and early customers to define what constitutes strong evals in different domains
• Work with Protege researchers to design and build benchmarks
• Build the standards on how different modalities should be processed
• Own infrastructure by building the backend the vertical runs on which includes data pipelines, execution environments, storage, and orchestration
• Stand up sandboxed environments for agentic evals, where models need tools, code execution, or multi-step tasks
• Go from fast iteration to product by finding repeatable eval patterns, infrastructure gaps, and product opportunities from live engagements
• Partner with DataLab (our research team) on domain-specific data and research questions

📌 Required Qualifications

• 4+ years of engineering experience
• Hands-on ML work evaluating models
• Have previously owned backend and infrastructure
• High ambiguity tolerance and bias to action
• Comfort working with urgency to meet the pace and volume of the market demands
• Strong written communication

⭐ Desirable Experience

• Prior experience building benchmarks, evals, or human data pipelines for LLMs
• Time at a frontier lab, an eval-focused team, or a research org
• Founding or early engineer experience at a fast-moving startup
• Familiarity with agentic systems, RL environments, code-execution sandboxes, TEE/TREs

🎁 Benefits

Competitive

🛂 Visa & Eligibility

Not specified