Top Senior Machine Learning Engineer Jobs in Boston, MA

Build and productionize large-scale ML models and pipelines for query understanding and personalization. Work on autocomplete, query tagging/NER, query expansion, intent modeling, and LLM-powered natural-language search. Collaborate cross-functionally to design, deploy, and monitor batch and real-time systems, leveraging modern ML infrastructure and tooling to improve search relevance across Airbnb products.

The Senior Machine Learning Engineer will design, train, and deploy ML models for optimizing revenue in advertising, taking ownership of product development and leading the ML discipline.

Design, fine-tune, evaluate, and deploy LLM-driven, user-facing browser features using RAG, embeddings, prompt engineering, and modern ML tooling. Lead end-to-end model lifecycle, run production experiments, collaborate with product and engineering, and document and review work with an emphasis on privacy, latency, and usability.

The Senior ML Engineer will automate component placement on PCBs, working on R&D and production using ML and optimization techniques.

As a Senior Machine Learning Engineer, develop LLM evaluation frameworks and agentic AI workflows to extract insights from conversations and optimize customer experiences.

Lead training, adaptation, evaluation, and productionization of Lila's AI models for scientific customer workflows. Build evaluation loops, design experiments, debug model failures, integrate model improvements into product workflows, and create reusable tooling for model adaptation, evaluation, and deployment while collaborating across research and software teams.

The role involves managing the ML lifecycle, developing scalable ML systems, and collaborating on AI-first product solutions focusing on Large Quantitative Models.

The role involves owning and delivering AI-powered features for Calendly, collaborating with multiple teams, and applying machine learning solutions for product enhancement.

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.

Lead design, development, and production deployment of ML models and GenAI/LLM solutions to optimize clinical trial workflows. Build scalable pipelines, monitoring, and CI/CD for robust production use. Collaborate cross-functionally, mentor junior engineers, and communicate technical results to technical and non-technical stakeholders.

Design, implement, and scale ML-based solutions for search and advertising; define data collection, evaluation, and production serving strategies; analyze data to inform development; communicate cross-functionally; produce technical reports; and stay current with advances in LLMs, vision models, and generative AI.

As a Senior Machine Learning Engineer, you will enhance diffusion models, conduct research, collaborate on system implementations, and communicate findings effectively.

Build and ship full-stack features for a production AI/LLM platform: implement polished React UIs from specs, own database and access-control/security, integrate multi-provider LLMs (RAG, agentic workflows), and formalize QA by extending test suites with integration and end-to-end coverage.

As a Senior Machine Learning Engineer, you will develop and deploy AI-driven products to enhance clinical trials, collaborating with teams to improve patient care through scalable machine learning solutions.

Build and maintain backend services and infrastructure for ML and generative AI features. Develop scalable, resilient, high-performance Python services (async frameworks, streaming), deploy to Kubernetes, integrate RAG and LLM orchestration, instrument LLM observability, and collaborate cross-functionally to deliver production-ready AI products.

Build and maintain scalable ML and generative AI platforms and backend infrastructure; deploy and manage production applications; create services exposing ML/AI products; implement RAG solutions and vector databases; integrate LLM orchestration and foundation model APIs; collaborate cross-functionally and mentor teammates.

Lead end-to-end ML system development: data exploration, model research, training, deployment, evaluation, and MLOps. Collaborate cross-functionally to prioritize claims, improve matching, measure business impact, and own a technical workstream.

Design, develop, and deploy AI-driven Salesforce applications, autonomous agents, intelligent workflows, and decision-support capabilities. Implement Agentforce, Einstein AI, Prompt Builder, Data Cloud, LLMs, RAG, and integrations with enterprise systems. Build human-in-the-loop experiences, AI-native UX, analytics dashboards, and scalable architectures while ensuring security, governance, and Agile/DevSecOps practices.

Lead development of internal Python SDKs and agentic AI workflows, build scalable FastAPI ML services, maintain CI/CD with Jenkins/GitLab Runners, and use Galileo for model evaluation and observability to productionize autonomous systems.

Lead design and delivery of scalable AI systems for autonomous threat detection and response. Build and productionize LLMs, graph reasoning engines, streaming feature pipelines, and orchestration frameworks. Own evaluation, reliability, and cross-functional collaboration while mentoring engineers and experimenting with RAG, tool-calling agents, and multi-modal models.

As a Senior AI/ML Engineer, you will design and deploy LLM systems, collaborate with cross-functional teams, and manage model deployment in production environments.

Develop AI-powered solutions for personalized content and marketing. Collaborate with teams to optimize ML models and pipelines at scale, mentoring engineers and driving strategic growth initiatives.

Design, build, and scale MLOps infrastructure and CI/CD/continuous training pipelines. Deploy and monitor ML models as microservices or batch jobs, manage cloud GPU/CPU resources with IaC, enable observability for model drift and latency, and collaborate with data scientists, engineers, and product teams to ensure reproducibility, compliance, and production reliability.

Responsible for designing, building, and operating AI-powered features, scalable APIs, and maintaining integrations within ClickHouse's AI/ML ecosystem.

The Senior AI/ML Engineer will develop AI systems for enterprise decision-making, focusing on LLMs and real-time data processing. Responsibilities include building detection systems, insight engines, evaluation frameworks, and collaborating with engineers to ensure robust AI product engineering.