Machine Learning Engineer

About The Role The role owns end-to-end development of production ML systems: training, deployment, and monitoring of models that power core product features at scale. Work spans classical ML, deep learning, and LLM-powered features, with direct ownership of what ships to production. The team operates at the intersection of research and engineering — models here must be accurate, fast, cost-efficient, and reliable. This role sits in a well-funded, growth-stage environment where ML infrastructure decisions directly shape product outcomes. Key Responsibilities Design, train, and evaluate ML models for production use cases spanning ranking, classification, NLP, and generative AI features Build and maintain training and feature pipelines in Python, PySpark, and Airflow with clear data versioning and reproducibility standards Deploy and serve models using Docker, Kubernetes, and cloud ML platforms (SageMaker, Vertex AI, or equivalent), owning latency and cost targets Implement LLM-based features including RAG pipelines, prompt orchestration, and evaluation harnesses where applicable Establish monitoring for data drift, model degradation, and serving anomalies with automated alerting and retraining triggers Collaborate with data engineers, product managers, and applied scientists to translate business problems into well-scoped ML solutions Contribute to ML platform improvements: experiment tracking, CI/CD for models, and shared tooling What We Are Looking For 3–6 years of experience in machine learning engineering or applied ML, with multiple models shipped to production Strong Python engineering skills plus hands-on depth in PyTorch or TensorFlow Experience deploying and operating models on AWS, GCP, or Azure with real production SLAs Solid ML fundamentals: evaluation methodology, regularization, handling class imbalance, and offline/online metric alignment Experience with MLflow, Weights & Biases, or equivalent experiment tracking, and CI/CD for ML workflows Bachelor's or Master's degree in Computer Science, Statistics, Engineering, or equivalent practical experience Bonus: experience fine-tuning or serving open-source LLMs, vector databases, Kubernetes-based model serving, or a published paper or open-source ML contribution Show more Show less