Machine Learning Engineer

The role owns the design, training, and production deployment of machine learning systems that directly power core product features - spanning tabular ML, NLP, and increasingly LLM-based workflows.

Working in a small, senior team of ML engineers and data scientists, this person will tackle problems where model performance, inference latency, and reliability all matter - and ship systems that run in production, not just in notebooks.

• Design, train, and evaluate ML models (classification, ranking, NLP, and recommendation use cases) for production workloads
• Build and maintain feature pipelines in Python and SQL, ensuring consistency between training and online serving paths
• Deploy and version models using Docker, Kubernetes, and cloud ML platforms (Sage Maker, Vertex AI, or Azure ML), with automated CI/CD for model releases
• Instrument models for monitoring: track drift, latency, and business metrics with alerting and automated retraining triggers
• Collaborate with data engineering to build reliable data pipelines using Spark, Airflow, and modern data stack tooling
• Introduce and champion LLM-based capabilities where they add value - RAG systems, embeddings, fine-tuning - with rigorous evaluation
• Contribute to code reviews, design docs, and the team's ML engineering standards

• 3–7 years of experience in machine learning engineering, applied ML, or a closely related role, with multiple production model deployments
• Expert-level Python and hands-on depth in at least one major framework:
  PyTorch, Tensor Flow, or scikit-learn
• Production experience with cloud ML platforms and MLOps tooling (MLflow, model registries, CI/CD for ML)
• Strong ML fundamentals: feature engineering, evaluation design, regularization, and handling class imbalance and drift
• Experience with containerized deployment (Docker, Kubernetes) and serving systems such as Torch Serve, Triton, or FastAPI
• MS or BS in Computer Science, Statistics, Engineering, or equivalent practical experience