Top Machine Learning Engineer Jobs in Boston, MA

Build, deploy, and scale machine learning models and infrastructure for financial technology applications. Responsibilities include model development, feature and data pipeline engineering, cloud-based production deployment, monitoring, retraining, testing, CI/CD, model governance, and responsible AI practices. The role collaborates with Product and Data Science teams and uses distributed systems, containerized platforms, and cloud technologies to deliver resilient ML solutions.

Develop, fine-tune, train, and optimize computer vision and machine learning algorithms. Manage experiments, data, testing, validation, and reproducibility using MLOps tools. Research emerging ML techniques, analyze field-test data, and transition algorithms into production across cloud, on-premises, and embedded environments. Collaborate with engineers and national security customers to deliver mission-critical AI and natural language processing capabilities.

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.

Design, build, deploy, scale, and maintain machine learning models and platforms in production. Responsibilities include developing ML infrastructure, data pipelines, cloud architectures, distributed systems, model monitoring, retraining, testing, CI/CD, and responsible AI practices. The role collaborates with Product, Data Science, and Agile engineering teams while applying advanced modeling techniques and managing large-scale containerized ML systems.

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.

Develop and deploy production machine learning models and infrastructure at scale. Responsibilities include model development, data pipeline construction, cloud and Kubernetes operations, distributed data processing, CI/CD automation, model monitoring and retraining, responsible AI governance, and collaboration with product and data science teams. The role requires strong programming, machine learning, distributed systems, and production deployment expertise.

Develops, tests, evaluates, and deploys machine learning models using established architectures and frameworks. Supports feature engineering, data preprocessing, automated training and monitoring pipelines, and production AI integration. Collaborates with engineering and product teams on AI requirements, researches methods to improve model performance, and supports urgent IT incidents. The role requires strong Python and machine learning experience, knowledge of deep learning and generative AI, and advanced quantitative education.

Develop, test, deploy, and serve machine learning models and agentic AI capabilities. Build feature engineering, experimentation, training, monitoring, and REST-based model-serving workflows. Collaborate with data science, engineering, product, and MLOps teams to productionize AI solutions at enterprise scale using distributed computing, Databricks, cloud platforms, and containerized infrastructure. Support model performance improvements, generative AI initiatives, and urgent IT escalations.

Develop and optimize deep learning algorithms for resource-constrained embedded audio devices. Build software prototypes, deploy models, research efficient techniques such as quantization and pruning, evaluate models, and assess neural-network hardware accelerators. The role focuses on audio ML for headphones, earbuds, speakers, soundbars, and wearables, requiring embedded C/C++, Python, deep learning frameworks, and collaboration across teams.

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.

Six-month research co-op developing and evaluating multimodal sensor fusion, DSP, and machine learning algorithms for room environmental sensing. Responsibilities include collecting microphone, IMU, camera, and light-based sensor data; developing models, training datasets, and evaluation infrastructure; and implementing real-time prototypes. The role requires Python, MATLAB, TensorFlow or PyTorch, signal processing, deep learning, acoustic modeling, and real-time processing knowledge.

Machine Learning/DSP Research Engineer Intern developing and evaluating multimodal sensor fusion, digital signal processing, and machine learning algorithms for room environmental sensing. Responsibilities include collecting microphone, IMU, camera, and light-based sensor data; building machine learning models and training datasets; and developing evaluation infrastructure for real-time applications.

Develop agentic workflows and automation for real-time audio machine learning research. Build systems for data preparation, model training, hyperparameter optimization, evaluation, regression testing, experiment tracking, artifact management, and reporting. Operate GPU infrastructure and connect LLM agents to research tools, datasets, repositories, and compute resources. Collaborate with audio researchers to create reliable, reproducible workflows and observable dashboards.

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.

Build, deploy, scale, and maintain production machine learning models and infrastructure. Responsibilities include model development, feature selection, tuning, validation, data pipeline construction, cloud deployment, Kubernetes operations, CI/CD, monitoring, responsible AI, and production troubleshooting. The role collaborates with Product, Data Science, and Agile engineering teams to deliver resilient large-scale AI solutions.

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.