Applied Machine Learning Engineer

About the role

We are looking for an Applied ML Engineer to bridge cutting‑edge machine‑learning research and production software. You will read research papers, design experiments, evaluate results rigorously, and turn successful methods into reliable, user‑facing products.

Key responsibilities

• Reproduce and evaluate research methods using open‑weight and API‑accessible models.
• Design evaluation datasets, probes, scoring methods, baselines, and calibration tests.
• Build and extend evaluation infrastructure, including runners, judges, persistence, orchestration, and reporting.
• Turn research workflows into product experiences with configurable experiments, traceability, and review dashboards.
• Investigate verification methods under model modifications such as fine‑tuning, merging, quantization, and distillation.

Required profile

• Strong Python engineering skills with hands‑on experience in PyTorch and Hugging Face Transformers.
• Deep understanding of ML evaluation, dataset design, metrics, calibration, statistical uncertainty, and reproducibility.
• Ability to read research papers and implement methods from first principles.
• Experience building production‑grade software beyond notebooks, including APIs, async jobs, databases, logging, testing, and deployment.
• Comfort working across backend and frontend boundaries; product surface primarily React/TypeScript.

Required skills

• Python
• PyTorch
• Hugging Face Transformers
• ML evaluation and dataset design
• API development and asynchronous job handling
• Databases (PostgreSQL/pgvector)
• React
• TypeScript
• Next.js
• Data visualization and experiment dashboards
• DSPy, LiteLLM, Temporal, Ray, vLLM