Top Senior Machine Learning Engineer Jobs in Boston, MA

The Manager of Data Science will lead a team focused on machine learning model development, team management, and cross-functional collaboration to advance product goals.

Senior AI/ML Engineer responsible for translating federal supply chain and logistics problems into analytical solutions, developing forecasting, classification, and NLP models, and deploying them into production. The role owns model development, data pipelines, monitoring, retraining, drift detection, validation, documentation, and stakeholder communication. Work includes operating within government-controlled environments and meeting federal bias evaluation and documentation requirements.

Lead the design, training, evaluation, and deployment of large-scale machine learning models, including LLMs, diffusion models, and GNNs. Own distributed training pipelines, dataset interfaces, optimization strategies, model evaluation, serving, and lifecycle management. Collaborate with data, infrastructure, and scientific teams while providing technical leadership through architecture reviews, mentorship, and cross-functional decision-making.

Design, optimize, and deploy deep learning models for customer behavior prediction. Perform feature engineering, implement research-informed architectures, scale training pipelines, collaborate with client teams, document findings, and uphold engineering best practices.

Productionize machine learning systems for large-scale robotic fleets, including deployment infrastructure, predictive robot health monitoring, data pipelines, model validation, telemetry analysis, and fleet-wide rollouts. Collaborate with researchers and cross-functional engineering teams to deploy computer vision and ML models using cloud, edge, and GPU-accelerated platforms. Build simulation environments, dashboards, observability solutions, and scalable CI/CD processes while contributing hands-on Python development.

Build and deploy production machine-learning systems within client environments, covering data discovery, modeling, validation, deployment, scheduling, monitoring, and retraining. Develop forecasting and detection models for imperfect time-series data, establish honest evaluation and KPI frameworks, and ship usable APIs or interfaces. Work directly with client IT and data teams through discovery, demos, and feedback cycles. The role also involves data engineering, software development, basic cloud and DevOps work, and advising clients when ML is not appropriate.

Build and operate scalable ML infrastructure and platform capabilities across cloud and on-premises environments. Develop Python-based developer tooling, services, and production systems supporting experimentation, distributed training, model deployment, and operations. Lead complex initiatives from architecture through rollout, improve reliability and developer productivity, and partner with ML and infrastructure teams. The role emphasizes Kubernetes, AWS, distributed compute, GPU-intensive workloads, observability, and production excellence.

Lead the design, development, deployment, and monitoring of production AI/ML applications for dairy-farm intelligence. Responsibilities include data wrangling, feature engineering, model selection, experimentation, MLOps, agent evaluation, observability, fine-tuning, AI governance, and integration with cloud infrastructure. The role also drives AI adoption, establishes engineering practices, collaborates cross-functionally, and may build React and TypeScript user experiences.

Lead the design, development, deployment, and monitoring of production AI/ML applications for dairy farm intelligence. Responsibilities include data wrangling, feature engineering, model selection, experimentation, MLOps, agent evaluation, observability, fine-tuning, AI governance, and adoption enablement. The role also involves collaborating on React and TypeScript user experiences, translating ambiguous requirements into scalable systems, and communicating technical tradeoffs across product, engineering, and dairy science teams.

Design and scale machine learning infrastructure across AWS and on-premises environments. Lead MLOps architecture, automation, CI/CD, deployment, monitoring, and reproducible ML workflows using Kubernetes, Airflow, and related tools. Collaborate with engineering and data science teams, evaluate MLOps technologies, ensure security and compliance, conduct design reviews, enforce engineering standards, and mentor junior engineers.

Develop and maintain open-source Python frameworks and ML developer tools, architect scalable libraries, grow early-stage projects, build real-time streaming capabilities, integrate modern frontend technologies, and collaborate with open-source contributors and the broader developer community.

Build and operate production machine learning infrastructure, including distributed GPU training, Kubernetes orchestration, cloud platforms, MLOps pipelines, observability, and large-scale data workflows. Optimize and deploy models to embedded edge hardware using quantization, ONNX, and TensorRT, with OTA updates and rollback. Translate research prototypes into reliable production systems, create design documentation and runbooks, mentor engineers, and drive engineering practice adoption.

Build and scale machine learning infrastructure, training systems, inference workflows, and agentic AI tools for protein design research. Improve distributed training performance, GPU utilization, reliability, and scalability while partnering with AI scientists and protein engineers. Translate research prototypes into reusable production systems, contribute to technical direction, evaluate emerging ML tools, and support scientific communication.

Build and improve machine learning systems for ad ranking, relevance, and optimization. Responsibilities include developing active learning and LLM-assisted labeling workflows, owning experimentation and evaluation, and deploying low-latency inference services. The role partners with product, data, and platform teams to improve advertiser performance and user engagement while ensuring production reliability, scalability, latency, and data quality. Candidates will use Python, Go, AWS, distributed systems, and AI-assisted development tools.

Own the full lifecycle of clinical text extraction models, from problem framing and annotation strategy through production deployment, evaluation, monitoring, and retraining. Build clinical NLP pipelines, establish defensible quality and error-analysis systems, and apply LLMs using rigorous evaluation, observability, and cost controls. Partner with clinical data and backend engineering teams while addressing PHI protection, de-identification, auditability, and access controls. As the first ML hire, set technical direction and potentially build and lead the future ML team.

Lead design, training, fine-tuning, and deployment of ML and generative models (LLMs, diffusion). Build scalable MLOps pipelines, vectorization and data ingestion, integrate models into microservices for low-latency inference, implement evaluation/alignment/bias-mitigation, optimize models (quantization, distillation), and mentor engineers while influencing the AI product roadmap.

As a Senior Machine Learning Engineer, you'll build and deploy ML models for risk and fraud detection, leading technical efforts and collaborating cross-functionally.

Design, build, and deploy high-throughput ML and LLM-powered personalization and agentic systems at consumer scale. Process petabyte-scale data, optimize real-time inference, implement resilient fallback paths, and balance latency, cost, and model quality while collaborating across engineering and product teams.

The Senior Machine Learning Engineer will develop core ML models, manage data pipelines, and collaborate with stakeholders to deliver scalable ML solutions.

The Senior Machine Learning Engineer will build and own production ML systems, manage end-to-end workflow, debug issues, and mentor others.

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 Senior ML Ops Engineer, you will bridge Data Science and Engineering to automate ML workflows, manage CI/CD for ML systems, and collaborate with various teams to implement solutions for AI features and services.

The Senior Machine Learning Engineer will develop, deploy, and optimize large language models on healthcare data, collaborate with teams, and ensure compliance.

Build and deploy AI and machine-learning capabilities for cyber threat hunting, anomaly detection, incident response, detection engineering, and security automation. Develop custom models, LLM applications, and agentic systems, managing the full production lifecycle from data preparation through monitoring. Partner with security, engineering, offensive security, and governance teams to evaluate emerging technologies, support responsible AI, advise leadership, and mentor technical staff.

Build and scale ML development infrastructure and Agentic AI tooling. Optimize distributed training (PyTorch/DDP), improve GPU utilization, design multi-cloud Kubernetes environments, and collaborate with ML researchers and engineers to streamline the ML lifecycle.