Applied ML Engineer

The Role

We’re looking for an Applied ML Engineer to build systems at the intersection of machine learning research and production software.

This is an end-to-end engineering role. You should be comfortable reading a research paper, identifying what is actually testable, building the smallest useful experiment, evaluating it rigorously, and turning the result into a production system that users can interact with.

You’ll work across model evaluation, model internals, inference infrastructure, backend systems, and product interfaces. The goal is not simply to reproduce research. It is to turn promising methods into reliable, measurable, and usable products.

What You’ll Do

• 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 directly with model weights, logits, hidden states, activations, model APIs, and inference infrastructure when required.

• Build and extend our evaluation infrastructure, including runners, judges, persistence, experiment orchestration, and reporting.

• Turn research workflows into product experiences, including experiment configuration, runs, traces, comparisons, reports, and review workflows.

• Investigate how verification methods behave under model modification, including fine-tuning, merging, quantization, distillation, safety removal, and deliberate evasion.

• Design controlled experiments that separate meaningful signals from artifacts or confounders.

• Write clear technical reports that distinguish measured evidence, interpretation, and hypotheses.

• Ship production-quality systems with APIs, background jobs, observability, testing, and documentation.

What We’re Looking For

• Strong Python engineering skills and hands-on experience with PyTorch and Hugging Face Transformers.

• A strong understanding of ML evaluation, including dataset design, baselines, metrics, calibration, false positives, false negatives, statistical uncertainty, and reproducibility.

• Ability to read ML research papers and implement methods from first principles rather than relying entirely on existing packages.

• Experience building production software beyond notebooks, including APIs, asynchronous jobs, databases, logging, testing, and deployment.