Top Machine Learning Engineer Jobs in Boston, MA

Designs, builds, and operates production-grade agentic AI systems and orchestration frameworks. Responsibilities include prompt architecture, tool and API integrations, monitoring, evaluation, cost controls, reliability improvements, and governance documentation. The engineer collaborates with data science, data engineering, and governance teams to ensure reliable, compliant workflows using structured healthcare and pharmaceutical data.

Build and evaluate machine learning models, agentic systems, LLM applications, and pipeline components under senior-engineer guidance. Interns will gain experience across the ML lifecycle, including problem framing, model selection, development, evaluation, and deployment support. The role involves collaborating with ML Engineers, Product Managers, Designers, and Full-Stack Engineers on scalable AI solutions.

Develop and maintain production machine learning models, build scalable training and serving technologies, optimize real-time and batch ML pipelines, and monitor model performance and drift. The role involves deep neural networks, recommendation systems, automated retraining, ML research, engineering excellence, and communicating technical concepts to diverse audiences. Candidates need 6+ years of industry ML experience, strong coding skills, and experience deploying neural networks at scale.

Develop advanced personalization and recommendation systems for digital grocery shopping. Build, deploy, monitor, and maintain machine learning pipelines and microservice APIs supporting production inference. Apply natural language processing, generative AI, large language models, agentic AI, recommendation models, deep learning, transformers, and classical machine learning techniques to real-world business problems. Evaluate and tune models, communicate technical results, and contribute to scalable AI solutions.

Own machine learning problems end to end, including data, modeling, evaluation, deployment, monitoring, and production optimization. Build training, inference, evaluation, datasets, and benchmarking pipelines for indoor positioning and item localization. Collaborate with applied scientists, engineering, and product teams to deliver enterprise features while optimizing accuracy, coverage, latency, and cost. Contribute reusable infrastructure, architecture decisions, and high engineering standards.

Lead the technical direction for machine learning algorithms and foundation models that analyze autonomous driving data. Develop semantic modeling, multimodal scene understanding, causal behavior modeling, and data-mining systems for L4 autonomy. Mentor senior engineers, influence multiple teams, and partner with infrastructure groups to deploy scalable evaluation platforms. Requires expert Python, Linux, and modern ML framework skills, with substantial experience in machine learning, robotics, or autonomous systems.

Designs, builds, and operates production machine learning systems at scale. Develops deep learning models for sequential, multimodal, and spatio-temporal data supporting risk assessment, engagement, and event detection. Partners with research, product, and platform teams to deploy reliable ML products across cloud and edge environments. Responsibilities include software engineering, AWS-based deployment, complex SQL, large-scale data processing, model orchestration, and cross-functional delivery of measurable mobility and road-safety impact.

Leads the design and development of enterprise-scale AI/ML data platforms, distributed processing systems, cloud-native applications, ML pipelines, microservices, RAG solutions, and vector data systems. The role includes AWS infrastructure, Kubernetes, CI/CD, observability, database optimization, production troubleshooting, technical architecture, mentorship, and engineering governance.

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, build, and deploy scalable ML pipelines for multimodal neuroscience data. Research, train, and deploy advanced deep learning and reinforcement learning models to predict brain states, detect pathological patterns, and generate personalized neuromodulation parameters. Select AI frameworks and cloud infrastructure, collaborate with computational neuroscientists, and contribute to IP and publications.

The Staff Machine Learning Engineer will design and build scalable ML systems, mentor engineers, and drive AI strategy at SailPoint, focusing on AI-powered identity security solutions.

Design, build, and productionize agentic AI systems including LLM orchestration, retrieval, and evaluation. Work across the ML lifecycle from experimentation to deployment and monitoring, leverage proprietary structured and unstructured financial data, and collaborate with data, product, design, and MLOps teams to deliver scalable, robust agentic solutions.

Develop, implement, and deploy scalable machine learning models for healthcare solutions. Collaborate cross-functionally to integrate models, analyze data to improve performance, ensure data quality, and stay current with ML advancements to support patient care and operational efficiency.

Lead technical direction for high-impact machine learning initiatives, from research and experimentation through production deployment. Build and fine-tune LLMs, document understanding systems, model-serving infrastructure, evaluation frameworks, and data feedback loops. Establish standards for model quality, calibration, reproducibility, and responsible deployment while mentoring ML engineers and influencing organizational roadmaps. The role requires hands-on software and ML engineering expertise, production-scale model development, and strong statistical judgment.

Own the research-to-production pipeline: harden training/evaluation workflows, build packaging/deployment tooling, design release gates, optimize inference and serving for latency/throughput, establish benchmarking and validation, and instrument feedback from production to accelerate research iteration.

Develop and deploy machine learning solutions for generative AI platforms, LLM-powered applications, and AI toolkits. Interns will solve agentic design and LLM orchestration challenges, prototype and evaluate models, develop pipeline components, and support deployment across the ML lifecycle. The role involves collaboration with ML engineers, product managers, designers, and full-stack engineers, with mentorship and opportunities to work on scalable, production-ready systems.

Design, implement, and scale end-to-end retrieval-augmented generation (RAG) pipelines. Build and optimize retrieval systems, embeddings, vector search, and LLM orchestration. Collaborate with product, design, and MLOps to productionize robust ML systems across large proprietary datasets.

Develop, evaluate, and deploy AI/ML models for national security applications. Analyze complex datasets, build scalable backend services and APIs, create visualizations and UIs, and collaborate with researchers, engineers, and customers to transition prototypes into production across cloud, on-prem, and edge environments.

Design, develop, and maintain features for Gradio and Trackio, architect Python frameworks, collaborate with contributors, and enhance user experience for ML web apps.

Build and improve applied AI models for scientific customer workflows. Responsibilities include model post-training, evaluation loop development, experiment design, debugging model behavior, and integrating AI capabilities into production software. The role collaborates with AI researchers and software engineers to adapt models, create reusable tooling, and translate customer feedback into model improvements.

Develops and productionizes deep learning and machine learning perception models for robotics and recycling sortation. Responsibilities include experimenting with neural network architectures, designing computer vision solutions, conducting statistical experiments, deploying successful models, collaborating with data, modeling, and cloud infrastructure teams, and improving ML infrastructure.

Design, build, and deploy K-12 AI features (lesson planning, grading, tutoring, admin analytics) while ensuring FERPA/COPPA compliance and child-safety guardrails. Measure impact, iterate with curriculum and legal partners, and maintain production-grade documentation, testing, and runbooks for deployed AI systems.

Develop, train, evaluate, and deploy machine learning models and AI-powered applications. Responsibilities include data preprocessing, feature engineering, deep learning development, cloud-based model deployment, and collaboration with data engineers, MLOps teams, and business stakeholders. The role also requires applying strong analytical and research skills while staying current with advances in artificial intelligence.

Develop and productionize large-scale machine learning models and pipelines for Airbnb search, ranking, recommendation, and personalization. Build batch and real-time systems using structured and unstructured data, improve model serving and experimentation efficiency, and collaborate with engineering, product, operations, and data science teams. Drive technical decisions, quantify business impact, and lead multiple machine learning initiatives across teams.

Build and operate production machine learning training systems on Amazon SageMaker using AWS Trainium. The role focuses on optimizing PyTorch code for Trainium hardware, understanding NeuronCore compilation, diagnosing device-level issues, tuning distributed training for cost and throughput, and delivering production pipelines. The engineer will collaborate directly with client and internal teams in a forward-deployed engineering environment.