Machine Learning Engineer

• Design and build scalable ML services for enrichment workflows, including model training pipelines and high-performance inference APIs • Deploy and optimize models using modern inference libraries and frameworks (ONNX, vLLM, TensorRT, etc.) to achieve low-latency, high-throughput performance • Collaborate with software engineers and product teams to define data requirements, feature engineering strategies, and model evaluation metrics • Build robust monitoring, observability, and evaluation systems to ensure model quality and service reliability in production • Stay current with emerging ML techniques, tools, and best practices, particularly in areas like model optimization, efficient inference, and large-scale data processing • What we look for • What we look for • 5+ years of experience building and deploying machine learning systems in production environments • Strong proficiency with model deployment technologies (Kubernetes, Ray, etc.) and inference libraries (ONNX, vLLM, TensorRT, or similar). Proficiency with model training frameworks (PyTorch, TensorFlow, Jax) • You've successfully designed and scaled ML services that process large volumes of data and serve predictions with strict latency and throughput requirements • Experience with the full ML lifecycle, including data preprocessing, feature engineering, model training, evaluation, deployment, and monitoring • Solid software engineering skills, including experience with distributed systems, APIs, and cloud infrastructure • You have a passion for building reliable, performant ML systems and understand how they create value for end users • U.S. Person status is required as this position will require the ability to access U.S only data systems