Top Remote Senior Machine Learning Engineer Jobs in Boston, MA

Lead the development of healthcare data products using machine learning. Design models, collaborate with teams, and mentor others in statistical methods.

The Senior Machine Learning Engineer will develop ML APIs, build data pipelines, and collaborate across teams to enhance ML models and applications.

The Sr. Machine Learning Engineer will develop and deploy ML solutions for healthcare, manage data pipelines, and work with large datasets to enhance healthcare delivery.

As a Senior Machine Learning Engineer, you'll design and develop workflows utilizing vision-language models, manage data pipelines, and collaborate with multiple teams to enhance model accuracy and efficiency.

The Senior Machine Learning Engineer will design, implement, and optimize machine learning models, drive AI initiatives, and collaborate cross-functionally to enhance SailPoint's AI capabilities.

The role involves designing, building, and deploying computer vision models for vehicle damage detection, covering the entire ML lifecycle and collaborating across teams.

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

You will train, evaluate, and deploy ML models for scene understanding and behavior prediction in autonomous vehicles, while collaborating with engineers.

The role involves designing and implementing advanced analytics solutions for healthcare payers, analyzing complex problems, mentoring juniors, and building client relationships.

The Senior Machine Learning Engineer will develop, deploy, and optimize large language models on healthcare data, collaborate with teams, and ensure compliance.

The Senior Machine Learning Engineer leads technical direction for ML systems focused on personalization and ranking, overseeing architecture and implementation while mentoring team members.

Design, build, and own production ML and LLM-driven features (RAG, prompt engineering, fine-tuning, semantic search). Write maintainable code, create design docs, troubleshoot issues, collaborate with product/design, and participate in Agile development. Work with Python/Java/JavaScript, ML frameworks, and cloud/container technologies.

Design, build, and operate end-to-end ML pipelines including data ingestion, feature engineering, training, deployment, and monitoring. Deploy and scale models on AWS or GCP, implement CI/CD, containerization, orchestration, model lifecycle management, observability, and mentor junior engineers to productionize personalization, recommendation, and NLP solutions.

Design, train, and deploy scalable ML models (generative, classification, regression). Define feature roadmaps, collaborate with engineering to implement code and APIs, adapt ML methods for distributed/GPU environments, and create evaluation/annotation programs for fine-tuning and performance measurement.

Design, train, evaluate, and deploy predictive transportation and delivery ML models (primarily XGBoost). Perform EDA, feature engineering, experiment design, and statistical evaluation. Build production ML pipelines and MLOps practices on Google Vertex AI. Collaborate with data engineering and supply chain stakeholders to translate operational needs into scalable solutions.

Design, build, and own production ML and LLM-powered product features (chat, RAG, semantic search, data extraction). Write maintainable code and docs, debug issues, collaborate with product and design, follow Agile practices, and stay current on AI/ML methods.

Own and improve recommendation and search systems across feed, discovery, and search. Build retrieval and ranking (candidate generation, embeddings, two-tower models), run experiments, create user/content/session representations, and ship production ML systems with monitoring to improve retention, engagement, and content distribution.

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.

The Machine Learning Engineer will build data-intensive products, deploy ML models, analyze datasets, and collaborate with product teams to enhance Nextdoor's platform.

The Senior Machine Learning Engineer will manage the full ML lifecycle, collaborating with teams to develop scalable systems that improve product decisions and empower monetization strategies.

As a Senior ML Engineer, you'll own ML-driven features from modeling to deployment, drive innovation in generative AI, and mentor junior members.

The role involves designing, training, and evaluating ML models, optimizing performance, and collaborating with teams to implement AI technologies in products.

Develop and deploy advanced ML solutions for OCR and NLP, optimizing models for performance and integrating them into production workflows while collaborating with cross-functional teams.

The Senior Machine Learning Engineer will utilize deep experience and insights to enhance communication and digital interactions through advanced machine learning tools and algorithms, focusing on unstructured data.

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.