Top Machine Learning Engineer Jobs in Boston, MA

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.

Develop, optimize, and deploy audio and multimodal machine learning models on embedded and edge devices. Build real-time inference pipelines, convert models to efficient C/C++ implementations, and optimize latency, memory, SRAM, and power across MCUs, DSPs, NPUs, and accelerators. Collaborate with researchers, DSP experts, firmware engineers, and hardware teams to deliver production-ready embedded AI systems using model development, quantization, profiling, and on-device runtimes.

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.

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.

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, build, deploy, scale, and maintain production machine learning systems and models. Lead technical design, model and application development, data pipeline construction, cloud architecture, testing, CI/CD, monitoring, governance, and responsible AI practices. Collaborate with Product, Data Science, and Agile engineering teams while leading teams developing enterprise AI coding tools.

Leads the design, development, deployment, and maintenance of machine learning systems at scale. Responsibilities include building ML models and data pipelines, writing production code, optimizing cloud architectures, automating testing and deployment, monitoring and retraining models, and ensuring performance, governance, security, responsible AI, and explainability. The role collaborates with Product and Data Science teams in an Agile environment focused on enterprise AI coding tools.

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.

Lead end-to-end ML systems for identity verification, including document authenticity, face match, liveness and deepfake detection. Build GNN-based identity graphs, behavioral/device-intelligence models, vendor benchmarking/evaluation, and production enforcement. Mentor engineers and align ML architecture with Product, Compliance, Risk, and Security stakeholders.

Design, build, and productionize scalable machine learning systems for personalized health and fitness metrics using physiological and behavioral data. Responsibilities include improving ML pipelines, translating research prototypes into reliable production services, developing operational infrastructure, implementing observability and incident response, and mentoring engineers and applied scientists.

Build and operate production AI systems powering WHOOP Coach, AI Support, and other intelligent product features. Lead AI/ML initiatives across data pipelines, dataset design, LLM fine-tuning, evaluation, deployment, experimentation, observability, and continuous feedback loops. Partner with product and data science to create reliable systems based on member needs and real-world usage. Mentor engineers and data scientists while establishing best practices for applied AI/ML, privacy, security, and production lifecycle management.

Design, train, test, optimize, and deploy machine-learning and deep-learning models using time-series and biosensor data. Develop robust inference systems for constrained edge and cloud environments, monitor algorithm performance and data quality, support regulated health-feature documentation, and conduct applied research. Collaborate with data science, firmware, and engineering teams to translate research prototypes into scalable production features for wearable technology.

Lead technical design and delivery of production AI/ML and GenAI solutions (RAG, LLMs, agents). Build, tune, deploy, and monitor models and APIs, own MLOps practices, operate cloud AI workloads, ensure software quality and responsible AI, and partner with cross-functional stakeholders while mentoring engineers.

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.

Lead development of advanced ML models and production-grade systems to optimize pricing, allocation, and auctions in Roku's ad serving platform. Translate business problems into hypotheses, run live experiments, mentor teams, and collaborate cross-functionally to drive marketplace intelligence and demand optimization.

Design and deploy production-grade ML systems, integrate models into software pipelines, and develop features leveraging ML for optimization and prediction problems.

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.

As a Senior AI/ML Engineer, you will design and deploy LLM systems, collaborate with cross-functional teams, and manage model deployment in production environments.

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.

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.

Senior-level engineer delivering analytics and ML-Ops solutions for healthcare payers. Design and implement analytics platforms on Databricks, develop CI/CD and provisioning patterns, collaborate with data scientists, mentor juniors, and build client relationships to drive actionable insights and operational improvements.

Lead the technical strategy and hands-on development of scalable machine learning systems for Spotify’s Discovery Mode and royalty programs. Design, evaluate, deploy, and optimize production ML solutions that help artists reach fans. Provide architectural leadership, collaborate across product, engineering, research, design, and data science, prototype new approaches, improve system reliability, and mentor engineers.

Design, build, and deploy autonomous AI agents using LLMs and agent frameworks; integrate agents into e-commerce and supply chain systems; implement multi-agent orchestration, memory and tool integrations; fine-tune models and apply RL concepts; and build safety, monitoring, and MLOps pipelines.