Machine Learning Engineer

About The Role The role focuses on building and shipping machine learning systems that run in production — not prototypes. Expect to own models end-to-end: data pipelines, training infrastructure, deployment, monitoring, and iteration based on real-world performance. The team works on high-stakes problems where inference latency, cost efficiency, and model reliability matter as much as offline metrics. This role sits at the intersection of software engineering and applied ML, and directly shapes how models reach users at scale. Key Responsibilities Design, train, and productionize ML models across ranking, classification, and forecasting workloads using PyTorch and scikit-learn Build scalable data and feature pipelines in Python and Spark, ensuring consistency between offline training and online serving environments Deploy and operate models on cloud infrastructure (AWS or GCP) using containerized services, Kubernetes, and CI/CD pipelines Implement model monitoring and alerting for data drift, latency regressions, and prediction quality degradation Design and run online experiments — A/B tests and shadow deployments — to validate model improvements against business metrics Optimize inference performance through model compression, quantization, batch tuning, or hardware-aware serving strategies Contribute to engineering standards through rigorous code reviews, system design docs, and shared tooling What We Are Looking For 3–7 years of experience in machine learning engineering or a closely related field, with multiple models shipped to production Strong Python engineering skills and deep hands-on experience with PyTorch or TensorFlow Production experience with cloud ML platforms: AWS SageMaker, GCP Vertex AI, or equivalent self-managed serving stacks Solid MLOps fundamentals: experiment tracking (MLflow or W&B), model versioning, CI/CD for ML, and infrastructure-as-code Strong grasp of ML fundamentals: evaluation methodology, regularization, class imbalance, and distribution shift BS in Computer Science, Engineering, Statistics, or equivalent practical experience; MS/PhD a plus Bonus: experience with LLM fine-tuning and serving (vLLM, Triton), streaming data systems (Kafka, Flink), or distributed training (DeepSpeed, Ray) Show more Show less