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

The Staff Machine Learning Engineer will lead ML systems development for personalization and ranking, design architectures, and mentor team members, ensuring reliable operations at scale.

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 role involves leading machine learning model design, AI development, data analysis, and enhancing digital workplace experiences at CVS Health.

Lead customer-facing projects to design, build, and productionize end-to-end data and AI solutions on Databricks. Own architecture, implement data pipelines, ML/AI integrations, applications, and reusable assets. Embed with customers, guide adoption, manage technical delivery, and collaborate cross-functionally to drive measurable outcomes.

Develop and maintain geometry processing, simulation, and rendering algorithms; build services to read/process 2D/3D engineering data; curate large datasets; implement ML post-training workflows; and contribute to domain-specific languages for engineering.

Lead enterprise-level strategy and delivery for building design, engineering, data readiness, and AI adoption. Define customer success plans, run workshops, shape consulting offerings, mentor staff, drive change management, and ensure high-quality project delivery and measurable customer value.

As a Machine Learning Scientist at Nucs AI, you will develop innovative ML methods for medical image analysis, collaborate with clinicians, and contribute to clinical validation studies, impacting cancer diagnostics.

Embed with strategic healthcare customers to build and deploy production AI applications on Infinitus platforms. Deliver deployment artifacts (MCP servers, sub-agents, workflows), provide white-glove enterprise support, codify repeatable patterns, and guide safe LLM/agent adoption while collaborating with Product and Engineering.

Own end-to-end matching and ranking systems for out-of-home advertising: design candidate generation and re-ranking models, build and manage data warehouse and pipelines, implement evaluation and monitoring, publish low-latency ranking APIs, and maintain model observability and retraining workflows.

Build, deploy, monitor, and document predictive models and analytical reports; translate findings for non-technical stakeholders; ensure model quality, fairness, and explainability; collaborate with data engineers and train staff on data literacy; support production ML infrastructure on AWS.