Top Hybrid Machine Learning Engineer Jobs in Boston, MA

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.

Lead design, build, deploy, and support of foundational AI systems including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimize production LLM performance (cost, latency, throughput) and contribute to long-term technical vision.

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.

Build and validate ML and statistical models, perform exploratory and descriptive analyses, create visualizations, maintain data pipelines and quality, collaborate with clients to deliver tailored AI/ML solutions, mentor junior staff, and apply modern AI techniques including generative AI and NLP to solve business problems.

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.

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.

Lead development of motion planning and prediction ML models for Level 4 autonomous vehicles. Design and productionize behavior models using deep learning, generative AI, and reinforcement learning. Deploy solutions to vehicle fleets, collaborate with perception, simulation, data platform and integration teams, and report progress to leadership.

Designs, deploys, and evaluates scalable AI/ML models for R&D, clinical, and regulatory workflows. Partners with cross-functional stakeholders and data engineering teams to translate business needs into production solutions. Ensures data quality, governance, explainability, traceability, reproducibility, and validation. Researches emerging AI/ML techniques and enables colleagues across the organization. Requires strong machine learning, software engineering, cloud, database, communication, and problem-solving skills.

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.