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

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

Lead architecture and technical vision for integrations that ingest external data (e.g., Salesforce, HubSpot) into Jasper's IQ layer. Design scalable, secure data ingestion, define context primitives, drive cross-team alignment, and deliver staff-level initiatives that improve reliability and AI-driven product quality.

Serve as the primary technical owner for customer engagements, design and build integrations, write production-quality code, troubleshoot and ship scalable production solutions, capture precise product feedback, and partner with product and engineering to convert field learnings into roadmap improvements.

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.

Design, build, test, and improve plugin-based forecasting models from business need through production deployment. Own ML and modeling initiatives end to end, evaluate performance across customer environments, distinguish generalizable solutions from customer-specific requirements, and use AI-assisted engineering tools. Work autonomously with customers and cross-functional stakeholders in a distributed international team, with occasional travel.

Lead and grow a team of ML, backend, and data engineers to design, deploy, and operate production-scale personalization and recommendation systems. Set technical direction, partner with Product and Data Science, mentor engineers, prioritize delivery, and champion adoption of modern ML techniques including large language models and generative AI to improve user personalization.

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.

Build and scale machine learning infrastructure, training systems, inference workflows, and agentic AI tools for protein design research. Improve distributed training performance, GPU utilization, reliability, and scalability while partnering with AI scientists and protein engineers. Translate research prototypes into reusable production systems, contribute to technical direction, evaluate emerging ML tools, and support scientific communication.

The candidate will define and execute motion planning projects, develop behavior models using ML techniques, and collaborate with teams to enhance autonomous vehicle performance.

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.