Top Hybrid Machine Learning Engineer Jobs in Boston, MA

Design and deploy production recommendation and retrieval engines, build data orchestration pipelines for multiple modalities, implement agentic workflow frameworks, and prototype, optimize, and scale models with monitoring, governance, and low-latency SLAs.

Design, develop, and deploy scalable ML software systems for healthcare. Collaborate with teams to improve products and ensure performance and reliability.

Build, deploy, and maintain risk and eligibility models (credit, accident, fraud, behavioral). Manage modeling platform and vendor integrations, monitor model performance and drift, perform feature selection and validation, and collaborate with Risk, Finance, Product, and Engineering to set thresholds and translate results into actionable recommendations.

Implement and productionize ML research into performant C++ and Python systems, build ML infrastructure and training pipelines, design LLM/agent applications, and deploy AI workloads across edge, near-edge, and cloud environments while collaborating with data science and engineering teams.

Design and optimize on-vehicle perception inference: allocate CPU/GPU resources, compress and quantize large multimodal models, build TensorRT model conversion pipelines, and deliver production-quality low-latency C++/CUDA inference for power- and thermal-constrained vehicle SoCs.

Design, build, and optimize software applications and systems; implement software architecture and design patterns; improve performance, scalability, and reliability; collaborate with cross-functional teams; conduct code reviews and ensure adherence to best practices.

Lead training, evaluation, and deployment of ML models for scene understanding and behavior prediction in autonomous vehicles.

Own end-to-end matching and ranking systems for out-of-home advertising: design candidate generation and re-ranking models, build and manage data warehouse and pipelines, implement evaluation and monitoring, publish low-latency ranking APIs, and maintain model observability and retraining workflows.

Designs, develops, and updates statistical and machine learning models; automates analytical processes; documents assumptions and code; extracts data via SQL; participates in model/code reviews and communicates results to stakeholders.

Lead strategy, development, and production deployment of scalable AI/GenAI and ML solutions. Drive technical direction, mentor data scientists, own end-to-end initiatives, ensure MLOps and model governance, collaborate cross-functionally, and deliver measurable business impact using advanced models (LLMs, transformers, diffusion) and cloud platforms.