Machine Learning Engineer

Machine Learning Engineer

Data & AIUnited States (Remote)Full-time

Design, train, and ship machine learning and generative AI systems into production, where they actually move metrics, for enterprise clients and our own products.

You will own models from problem framing to production and monitoring. You will work close to the business, choosing the simplest approach that works, and you will care as much about reliability, latency, and cost as you do about accuracy. Expect a healthy mix of classical ML and modern LLM and RAG work.

* Frame business problems as ML problems and choose pragmatic, well-scoped approaches. * Build, train, and rigorously evaluate models, from gradient boosting to LLM and RAG systems. * Own feature engineering, training pipelines, experiment tracking, and reproducibility. * Package and deploy models behind reliable APIs with proper versioning. * Operate models in production with monitoring for drift, latency, cost, and quality. * Optimize the accuracy, latency, and cost trade-off for real workloads. * Integrate models into products in partnership with backend and data engineers. * Write clear documentation and help raise the team's MLOps maturity.

* 3+ years building and shipping ML systems to production. * Strong Python and fluency in PyTorch, TensorFlow, or scikit-learn. * A deep, practical understanding of the full ML lifecycle and honest evaluation. * Experience deploying and operating models on a major cloud (AWS, Azure, or GCP). * Comfort with APIs, containers, SQL, and working with messy real-world data.

* LLMs, RAG, vector databases, and prompt and output evaluation. * Docker, Kubernetes, MLflow, feature stores, and streaming data. * Experience with cost and latency optimization for inference at scale.

What success looks like

* In 30 days: you have shipped a model or a meaningful improvement to one. * In 90 days: you own an ML service in production with monitoring in place. * In 6 months: you are setting ML best practices that the team adopts.