Applied ML Engineer

You will bridge machine learning research and production software. You will reproduce and rigorously evaluate research methods, build experiment and evaluation infrastructure, work with model internals and inference systems, and turn research workflows into usable product experiences. You will ship production-quality systems with APIs, background jobs, observability, testing, and documentation.

Responsibilities

• Reproduce and evaluate research methods using open-weight and API-accessible models
• Design evaluation datasets, probes, scoring methods, baselines, calibration tests, and experiment harnesses
• Work with model weights, logits, hidden states, activations, model APIs, and inference infrastructure
• Build evaluation infrastructure including runners, judges, persistence, orchestration, and reporting
• Turn research workflows into product experiences for configuring, running, comparing, and reviewing experiments
• Investigate verification methods under model modification and deliberate evasion
• Design controlled experiments that distinguish meaningful signals from artifacts and confounders
• Write technical reports that separate evidence, interpretation, and hypotheses
• Ship production systems with APIs, background jobs, observability, testing, and documentation

Requirements

• Strong Python engineering skills
• Hands-on experience with PyTorch and Hugging Face Transformers
• Understanding of ML evaluation, dataset design, baselines, metrics, calibration, statistical uncertainty, and reproducibility
• Ability to read ML research papers and implement methods from first principles
• Experience building production software with APIs, asynchronous jobs, databases, logging, testing, and deployment
• Understanding of open-weight models and modern LLM inference systems
• Ability to work across backend and frontend boundaries
• Experience making experiments and results understandable through React and TypeScript interfaces