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

Lead end-to-end ML systems for identity verification, including document authenticity, face match, liveness and deepfake detection. Build GNN-based identity graphs, behavioral/device-intelligence models, vendor benchmarking/evaluation, and production enforcement. Mentor engineers and align ML architecture with Product, Compliance, Risk, and Security stakeholders.

Research, design, and implement discrete optimization and mission-planning algorithms for multi-agent autonomous systems; improve and maintain C++ mission-planning software; define technical roadmaps; mentor engineers; perform full software and hardware-in-the-loop simulations.

Design, build, and deploy production-grade ML systems for ICS/xOT cybersecurity, including threat detection, anomaly detection, NLP/LLM applications, and scalable data pipelines with observability and MLOps practices for cloud and on-prem environments.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

Lead end-to-end AI product discovery, prototype and ship working ML/AI solutions for HR, Marketing, and Commercial teams. Build first prototypes yourself, partner with engineers to productionize, define success metrics, and drive adoption and business impact.

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.

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

The Staff Machine Learning Engineer will lead ML initiatives, develop ML systems, collaborate with cross-functional teams, and mentor junior engineers to optimize healthcare intake workflows using advanced machine learning techniques.

Design, develop, and productionize multimodal AI systems (VLMs, LLMs, reasoning models) and end-to-end ML pipelines for geospatial data. Build training/experiment frameworks, synthetic datasets, scalable inference, monitoring, and collaborate with research, engineering, and product teams to deliver Earth intelligence solutions.

Build and deploy AI-powered, LLM-based solutions in client environments: implement integrations, develop production-ready code, support cloud deployments, prepare data and prompts, troubleshoot, and collaborate with client and cross-functional teams while documenting and transferring knowledge to enable sustainable production adoption.