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

Take Deepgram's speech models and optimize, compile, and run them on resource-constrained embedded and edge hardware. Work across model compression, runtime performance (C/C++/Rust), RTOS/bare-metal and embedded Linux environments, vendor NPUs/DSPs and inference runtimes, benchmarking, deployment pipelines, OTA updates, and partner with silicon vendors to enable real-time, low-power on-device voice experiences.

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

The Machine Learning Engineer develops production ML algorithms and recommendation systems, collaborates with teams, and optimizes user experiences.

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 Applied ML Scientist, you'll develop fraud detection models, research new fraud types, and write production code while collaborating across teams to enhance SentiLink's identity verification solutions.

Lead the development of machine learning systems for Cedar Pay's Personalization Engine, manage ML engineers, and ensure ML model effectiveness.

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, implement, and maintain ALM and CCAR/PPNR models for balance sheet forecasting. Build customer behavior, prepayment, and deposit models; monitor performance, perform backtesting and sensitivity analysis; collaborate with Model Risk, Market Risk, Treasury, and validation teams; prepare documentation and materials for governance and senior leadership; support balance sheet optimization and regulatory initiatives.

Embed with enterprise clients to discover, build, and deploy AI agent workflows against messy, unstructured data. Lead technical onboarding, construct connectors and pipelines, run production deployments, serve as primary technical contact, feed product insights back to engineering, and create repeatable implementation playbooks.

Lead large, cross-functional technical programs for autonomy, owning model development and release cycles. Act as DRI for behavioral capabilities, integrate Prediction and ML Planning, dig into ML/behavior issues, identify and mitigate technical risks, and align roadmaps with engineering, product, and executive stakeholders to deliver driverless milestones.