Top Remote Senior Level Machine Learning Engineer Jobs in Boston, MA

Lead design and delivery of AI/GenAI solutions: build and deploy scalable ML models, manage data pipelines and infrastructure, mentor teams, ensure data quality and compliance, and collaborate with stakeholders to drive AI-driven business outcomes.

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

Develop and optimize machine learning models for data extraction from tax documents, collaborate on software integration, and mentor junior team members.

Develop scalable machine learning services for data enrichment, managing the ML lifecycle from model training to deployment, and ensuring performance and reliability standards.

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

Build and operate Coinbase's LLM and agent platform: unified model gateway, AI Hub, secure microVM agent runtime, knowledge base integrations, AI FinOps, observability and evaluation tooling, and production-grade agent deployments with teams across the company.

Lead the SDL and Cash Line ML teams, developing innovative machine learning models to improve lending solutions and expand credit access. Collaborate with cross-functional teams to drive product strategy and enhance model performance, while mentoring a team of ML scientists.

As a Senior Staff Machine Learning Engineer at Affirm, you will lead complex ML system design and implementation, mentor engineers, and drive innovative ML strategies for financial services.

The Principal ML Engineer at Agero will develop and productionize a Dispatch System using ML models, optimize decision-making processes, and lead a team to improve operational performance.

Lead design, development, and deployment of enterprise AI agents and multi-agent systems. Architect scalable, secure AI/data systems, integrate vector DBs, embeddings, RAG, and cloud MLOps, drive automation/APIs, mentor engineers, set KPIs, and ensure observability, ethical AI, and production readiness.