Top Remote Machine Learning Engineer Jobs in Boston, MA

Build and own production Vision AI systems end to end, including model selection, inference pipelines, evaluation, monitoring, and customer-facing features. Shape architecture, collaborate with product, design, and engineering teams, and use AI coding tools to accelerate development. Establish high standards for quality, testing, reliability, security, and privacy while mentoring engineers and taking ambiguous ideas from prototype to production.

Architect and build Toast’s Vision AI platform, including video ingestion, processing, storage, inference pipelines, evaluation, and monitoring. Develop production ML services and customer-facing computer vision features, make architecture decisions, write significant code, and guide technical direction. Partner across teams, mentor engineers, establish reliability, security, and privacy standards, and promote AI-assisted development. The role requires deep distributed-systems and cloud expertise, production ML experience, video familiarity, and the ability to take ambiguous ideas from prototype to customer-ready products.

Apply established machine learning techniques—including clustering, classification, and anomaly detection—to ICS/OT cybersecurity data. Integrate model outputs into batch and near-real-time pipelines, evaluate third-party models, maintain reliable data contracts and observability, and troubleshoot production issues such as data drift and output quality. Collaborate with AI engineers, data engineers, and product teams while clearly communicating model behavior, uncertainty, and downstream use.

Lead the design and implementation of AI initiatives for edge devices. Develop and optimize machine learning solutions using large-scale sensor, video, text, diagnostic, transaction, and location data. Partner with engineering and business teams to prototype AI experiences, ship production systems, adopt research advances, and improve edge model performance. Mentor ML engineers and communicate technical concepts to varied audiences.

Design, develop, and operate scalable VoIP infrastructure supporting AI-driven voice workloads. Integrate telephony systems, LLM applications, and real-time communication platforms while improving observability, reliability, deployment, and SRE practices. Build tested, maintainable software across telephony and AI layers, support production environments, collaborate with engineering and product teams, and mentor colleagues.

Own the strategy, architecture, development, deployment, monitoring, and continuous improvement of production personalization and recommendation systems. Build custom embeddings, ranking models, and shared representations across feeds, search, and recommendations. Lead ambiguous problems from concept to measurable customer impact, establish AI evaluation standards, partner with product and data teams, make cross-team technical decisions, and coach senior engineers.

Leads the architecture and evolution of DraftKings’ machine learning platform, including real-time data pipelines, lakehouse governance, distributed data systems, and scalable ML serving infrastructure. Establishes engineering standards, drives complex initiatives, oversees reliability and cloud cost optimization, and partners across engineering and data teams. The role also mentors engineers, advances automation, supports incident response, and applies AI-assisted development practices.

Own and deliver goals for ML training and serving infrastructure. Lead engineers through ambiguous, distributed-systems challenges; design, develop, and operate highly available backend platforms; monitor reliability and support on-call operations. Collaborate with product and engineering stakeholders, improve code and design standards, identify and resolve technical issues, and mentor team members. The role focuses on scalable systems that enable production model training and serving.

Own and deliver quarterly engineering goals for Affirm’s ML Feature Platform. Design, develop, and launch scalable backend systems, lead engineers through ambiguous problems, collaborate with product and design stakeholders, improve platform reliability and availability, support on-call operations, establish quality standards, and mentor team members. The role focuses on highly available distributed systems supporting machine learning feature creation, storage, serving, and offline backfilling.

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.

Lead design, training, and deployment of large-scale ML models (LLMs, diffusion, GNNs). Own end-to-end training pipelines, distributed training, evaluation, and inference architecture. Collaborate with data and infra teams, drive reproducible model development, and provide technical leadership and mentorship from research through production.

Designs, deploys, and supports production machine learning solutions, including CI/CD pipelines, APIs, batch and real-time inference, model monitoring, data validation, and MLOps tooling. Partners with data scientists, actuaries, engineers, and business stakeholders to transition models into reliable applications. Responsibilities include troubleshooting, governance, testing, performance optimization, documentation, and providing technical guidance on production machine learning practices.

Build tutorials, reference solutions, and workshop content for ML inference, serving, and fine-tuning on AMD Instinct GPUs. Evaluate models and frameworks, develop platform examples, support technical events, collaborate with marketing on technical content, gather developer ecosystem insights, and contribute fixes to upstream projects.

Develop, deploy, troubleshoot, and optimize AI/ML components in secure production applications. Responsibilities include data preprocessing, feature engineering, LLM and RAG integration, prompt engineering, model evaluation, LLMOps monitoring, API integration, observability, and defect resolution for government and defense-adjacent customers.

Designs, builds, and deploys production AI agents and workflow automation systems. Responsibilities include agent orchestration, tool and API integration, retrieval, state management, permissions, safeguards, monitoring, testing, evaluation, and reliability improvements. The role partners with product and engineering teams to automate operational, patient coordination, and pharmacy workflows while maintaining healthcare-related privacy, authorization, auditability, and human approval controls.

Build and deploy production machine learning and recommendation systems for personalized healthcare platform experiences. Develop ranking models, feature pipelines, preference representations, model-serving APIs, evaluation workflows, and experimentation strategies. Monitor model quality, latency, reliability, and data issues while improving performance and handling cold-start scenarios. Collaborate with product, backend, frontend, and data teams to own personalization features through deployment. Ensure privacy-conscious processing, tenant boundaries, authorization, and approved clinical workflows.

Build and improve machine learning-powered search and discovery systems, including semantic and hybrid retrieval, ranking, query processing, indexing, APIs, and RAG support. Develop evaluation datasets and relevance metrics, analyze search quality, optimize latency and scalability, and implement monitoring and testing. Ensure permission-aware retrieval across tenant boundaries and collaborate with product and engineering teams to deliver reliable search features from development through continuous improvement.

Lead the design and production operation of Navanta’s AI platform, including RAG, retrieval, model serving, MCP tool orchestration, text-to-SQL, evaluation, and guardrails. Build auditable, secure AI systems with PII protection, prompt-injection defenses, grounded responses, and governed metric selection. Partner with data, platform, and product teams while optimizing model quality, latency, cost, and reliability for regulated banking environments.

Lead the design and development of production-grade machine learning systems, including large-scale document pipelines and agentic workflows for healthcare claims and payment integrity. Drive LLM prompt engineering, multimodal AI, RAG, orchestration, observability, reliability, and explainability. Collaborate with data science, data engineering, and product teams, reduce technical debt, improve scalability, and mentor engineers on enterprise AI system design and development.

Design and scale machine learning infrastructure for personalization, search, ranking, advertising technology, and real-time learning at consumer scale. Build distributed systems, data pipelines, feature infrastructure, model-serving systems, and low-latency decision systems. Lead architecture and zero-to-one initiatives, partner across product and engineering teams, mentor technical leaders, improve experimentation velocity, and explore LLM applications for personalization and conversational search.

Own quality engineering for an AI-enabled decision-support system. Develop test strategy, translate acceptance criteria into automated tests, integrate coverage into CI/CD release gates, and perform functional, integration, performance, and load testing. Build evaluation sets, audit model decisions, measure errors and overrides, document release evidence, and maintain rollback criteria. Coordinate user acceptance testing and independently govern releases. The role requires strong coding, statistical literacy, audit-ready documentation, active Secret clearance, and up to 25% travel.

Develop agentic workflows and automation for real-time audio machine learning research. Responsibilities include orchestrating training, evaluation, experimentation, reporting, data preparation, hyperparameter searches, benchmarking, regression testing, experiment tracking, artifact management, and reproducible execution. The role integrates LLM agents with research tools, datasets, repositories, evaluation suites, and GPU infrastructure while partnering with audio researchers to create reusable workflows and dashboards.

Develop, train, evaluate, optimize, and debug machine learning models for consumer audio applications. Focus on efficient AI and Edge ML by reducing latency, memory usage, and computational demands. Collaborate with software, embedded systems, and hardware engineers to deploy models on resource-constrained devices. Explore techniques such as quantization, pruning, and model compression while contributing to emerging AI technologies and practical product development.

Own the ML platform layer connecting data science and infrastructure. Responsibilities include managing the feature store, building self-service training and deployment platforms, standardizing retraining, scaling batch and online inference, implementing monitoring, and establishing standards for model and feature versioning, lineage, testing, and documentation. The role partners closely with data scientists and backend engineers to improve model delivery speed, reliability, and independence across multiple teams.

Build and evolve machine learning tooling and infrastructure for AI-powered clinical solutions, including automated prompt tuning, feedback loops, model benchmarking, experimentation, and evaluation. Partner with data science, clinical, software engineering, and product teams to develop scalable solutions, solve ambiguous technical problems, establish engineering standards, and provide technical mentorship without direct management responsibility.