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

Lead development and deployment of ML solutions for claims, including feature engineering, training/validation, MLOps, and LLM/vision models. Mentor junior scientists, run experiments, communicate results, and collaborate cross-functionally to deliver business value.

Lead and direct multidisciplinary teams to research, develop, test, integrate, and demonstrate advanced signal processing, optimization, and machine learning algorithms for radar, electronic warfare, and sensor systems. Oversee modeling/simulation, algorithm T&E, customer engagement, and technical briefings for defense and intelligence applications.

As a Staff Applied ML Scientist, you will develop and maintain fraud detection models, conduct research on fraud types, and collaborate across teams to drive product development and real-time decision-making.

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 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.

Build and productionize agentic workflows and RAG pipelines, integrate LLMs into the GTM stack, and develop high-performance backend services and ML pipelines. Connect quoting engine, Salesforce, and data lakes, solve synchronization and architectural challenges, and ensure observability, scalability, and reliability for sales automation tools.

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.

Lead design and delivery of low-latency market-making and automated trading strategies. Translate quantitative research into production systems, build backtesting and real-time decision engines, ensure scalability and observability, manage risk-aware optimization, and influence cross-functional teams on strategy and technical direction.

Lead AML model development, optimization, validation, and governance for transaction and customer screening across banking, brokerage, and lending. Build ML/statistical models, maintain vendor solutions, ensure regulatory compliance, document methodologies, produce AML dashboards, and support audits, risk assessments, and stakeholder communications.

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.