Top Hybrid Machine Learning Engineer Jobs in Boston, MA

Design, build, deploy, scale, and maintain production machine learning systems and models. Lead technical design, model and application development, data pipeline construction, cloud architecture, testing, CI/CD, monitoring, governance, and responsible AI practices. Collaborate with Product, Data Science, and Agile engineering teams while leading teams developing enterprise AI coding tools.

Build and maintain internal agentic AI platforms and developer-facing tools that integrate LLMs with engineering workflows. Partner with product and infra teams to design standards for agent deployment, reliability, evals, security, onboarding, and adoption measurement to improve developer productivity.

Provide technical leadership for CarGurus’ recommendation platform, defining architecture and building scalable personalized ranking and discovery systems. Partner with Data Science to productionize machine learning models, improve experimentation and recommendation quality, and deliver AI-powered consumer experiences. Lead complex cross-functional initiatives, improve reliability and observability, make organization-wide technical decisions, write code, mentor engineers, and uphold quality, security, and operational standards.

Leads WHOOP’s cloud-based machine learning organization responsible for sleep, recovery, and training algorithms. Manages ML scientists and engineers, establishes technical standards and development practices, guides architecture and deployment, and partners with platform, hardware, sensor, product, research, and engineering teams. The role requires scaling production ML systems, improving model validation, monitoring, and deployment, and translating physiological sensor capabilities into reliable consumer experiences.

Develop, optimize, and deploy audio and multimodal machine learning models on embedded and edge devices. Build real-time inference pipelines, convert models to efficient C/C++ implementations, and optimize latency, memory, SRAM, and power across MCUs, DSPs, NPUs, and accelerators. Collaborate with researchers, DSP experts, firmware engineers, and hardware teams to deliver production-ready embedded AI systems using model development, quantization, profiling, and on-device runtimes.

Perform Bayesian and probabilistic modeling, test and evaluate ML/AI systems, analyze complex datasets, develop algorithms, and transition solutions into software and real-time embedded systems for national security customers. Communicate results and propose technical paths forward.

Lead design and delivery of advanced data and ML solutions, performing exploratory analysis, statistical modeling, and visualization to inform strategy. Manage and coach teams, collaborate with clients to validate outcomes, and improve analytics processes and data quality to drive business decisions.

The Machine Learning Scientist will develop AI frameworks for predictive modeling in oncology, collaborating with teams to enhance drug R&D efforts through advanced machine learning techniques and multimodal data integration.

Design, implement, and scale end-to-end retrieval-augmented generation (RAG) pipelines. Build and optimize retrieval systems, embeddings, vector search, and LLM orchestration. Collaborate with product, design, and MLOps to productionize robust ML systems across large proprietary datasets.

Designs and operationalizes scalable machine learning platforms, pipelines, infrastructure, and production models. Develops MLOps and LLMOps solutions, including generative AI, prompt engineering, RAG, LLM fine-tuning, and computer vision applications. Collaborates with data scientists, engineers, architects, and cross-functional teams to optimize models and integrate them into enterprise systems. Applies hybrid ML and rule-based systems in regulated insurance environments, mentors colleagues, and supports Agile delivery.