Applied ML Engineer

What you'll do

• Reproduce and evaluate research methods; design datasets, metrics, experiments, and reports.
• Build evaluation infrastructure and product workflows, including APIs, experiment runners, and user-facing interfaces.
• Test verification methods across model modifications and produce reliable, documented evidence.

What they ask for

• Strong Python skills and hands-on experience with PyTorch and Hugging Face Transformers.
• Strong understanding of ML evaluation, including datasets, metrics, calibration, uncertainty, and reproducibility.
• Ability to read research papers and implement methods from first principles.
• Experience building production software beyond notebooks, including APIs, asynchronous jobs, databases, testing, and deployment.

Nice to have

• Experience in model provenance, fingerprinting, watermarking, distillation detection, red-teaming, safety evaluation, or interpretability.
• Experience with evaluation or inference infrastructure, React/TypeScript, data visualization, or serving open-weight models on GPUs.

Tools

Python, PyTorch, Hugging Face Transformers, React/TypeScript, DSPy, LiteLLM, Temporal, Ray

AI focus

Evaluate and verify models, including model provenance and behavior under fine-tuning, merging, quantization, distillation, safety removal, and deliberate evasion.