Machine Learning Engineer

About The Role

You’ll work on real customer ML problems: recommendation engines, classifiers, anomaly detectors, LLM‑powered features. You’ll partner with our MLOps engineers (so you’re not on your own when it’s time to ship) and customer data scientists (so you’re not flying blind on the domain).

• Translate customer business problems into ML problem statements with clear evaluation criteria.
• Train, evaluate, and harden models, including the unglamorous work of dataset curation and labeling pipelines.
• Build evaluation suites that catch regressions before they hit production.
• Build LLM‑backed features: prompt engineering, RAG, agent orchestration, eval pipelines.
• Pair with MLOps to ship; pair with QA to test; pair with security to threat‑model.

• 5+ years training and shipping ML models to production.
• Strong Python; deep knowledge of at least one of PyTorch, Tensor Flow, or JAX.
• Practical experience with evaluation: held‑out tests, online metrics, A/B frameworks.
• Track record of moving past notebooks: CI for training, model versioning, feature engineering at production scale.
• Written communication good enough to explain trade‑offs to non‑ML stakeholders.

• LLM eval frameworks (Tru Lens, Deep Eval, Promptfoo).
• Recommendation systems / classical ML at scale.
• Causal‑inference or experimentation experience.

Competitive global rate, scaled to experience and engagement. Profit‑share for full‑time hires.