Top Machine Learning Engineer Jobs in Boston, MA

Lead development, deployment, and maintenance of production ML and AI systems (including LLM inference, RAG, similarity search, observability, and governance). Collaborate cross-functionally to build data pipelines, fine-tune foundation models, apply responsible AI practices, and contribute technical vision for scalable, high-performance AI infrastructure in risk management.

Design, train, and deploy machine learning models for RF signal detection, classification, and vessel activity tracking in maritime intelligence.

As a Machine Learning Engineer II, you will enhance AI systems for automating customer operations, develop evidence extraction pipelines, and collaborate with teams on model development and deployment.

Refactor, debug, and improve Python code for clarity, robustness, performance, and maintainability. Design modular, reusable components, implement basic testing and validation, translate research/experimental code into production-quality modules, and collaborate with engineers using standard software engineering practices.

Lead the development of robot learning models, design simulation environments, optimize policies for real-world robots, and contribute to data lifecycle management.

Design, build, and operate scalable, reliable ML inference systems, backend services, and data pipelines for health features. Translate research prototypes to production, ensure monitoring/validation within a quality-managed SaMD environment, provide technical leadership, and participate in on-call rotations to maintain uptime and reliability.

Design, implement, and scale production-grade data and ML pipelines for risk and payments. Build ETL and feature engineering workflows, productionize models, establish observability and monitoring, participate in reviews and on-call rotations, and mentor junior engineers to ensure reliable, explainable decisioning systems.

The Principal AI/ML Engineer will lead the design, implementation, and deployment of AI systems, mentor junior engineers, and ensure model performance and reliability in production environments.

Lead a small ML engineering pod to turn research into production: design and operationalize training pipelines and low-latency inference services for computer vision, document intelligence, and predictive models. Drive R&D execution, benchmarking, prototyping, migrations from frontier models to fine-tuned/open-source solutions, and mentor early-career engineers while aligning research with product timelines.

Designs, implements, and deploys cloud-native AI/ML solutions and data pipelines to support personalization and optimization. Builds and serves ML models (real-time and batch), develops APIs and front-end tools, automates workflows, and ensures secure, scalable AWS infrastructure. Leads architecture discussions, mentors engineers, manages project lifecycle, and advises senior management on technical strategy and risk.

As a Senior Lead Machine Learning Engineer, you'll design and implement machine learning applications, lead teams, and optimize systems and data pipelines while collaborating in an Agile environment.

The Machine Learning Engineer will design and implement ML pipelines and productionize models, collaborating with scientists and engineers on scalable systems.

Design and build scalable end-to-end ETL/ELT data pipelines, process log and semi-structured data with Python and dataframes, and architect scalable database schemas (partitioning/sharding). Ensure data traceability, validation, monitoring, and enable datasets for machine learning model training and deployment while collaborating with data scientists and engineering teams.

The Research Engineer will collaborate with scientists to develop and validate machine learning models for speech technologies, manage scalable training systems, and design tools for accessibility to non-technical users.

Design and develop large-scale AI systems for healthcare, focusing on ML infrastructure, real-time analytics, and collaboration with teams. Requires extensive experience in software engineering and AI.

Design, develop, and deploy scalable ML software systems for healthcare. Collaborate with teams to improve products and ensure performance and reliability.

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

Build, deploy, and maintain production ML and AI systems (including LLMs, RAG, similarity search, guardrails, observability). Partner with cross-functional teams to design data pipelines, fine-tune models, monitor performance, and apply Responsible/Explainable AI best practices while contributing technical vision and roadmap for risk-focused AI products.

Lead training, adaptation, evaluation, and productionization of Lila's AI models for scientific customer workflows. Build evaluation loops, design experiments, debug model failures, integrate model improvements into product workflows, and create reusable tooling for model adaptation, evaluation, and deployment while collaborating across research and software teams.

Seeking a Machine Learning Engineer with 3 years of experience to develop and deploy ML systems, optimize models, and work collaboratively with teams to enhance ML capabilities.

Design and deliver machine learning models and cloud-based systems for frontline worker devices, including model training, optimization, and deployment in cloud environments.

The Staff ML Engineer will design and operate Samsara's ML platform, collaborating with teams to enhance ML features and improve safety outcomes. Responsibilities include overseeing system reliability, leading technical direction, and mentoring engineers.

Design and implement NLP and LLM-powered pipelines to extract, summarize, and semantically search technical documentation. Fine-tune transformer models, build RAG systems, convert unstructured documents into structured knowledge, optimize inference for production, and collaborate with engineers and SMEs to deploy document intelligence solutions.

Build, train, fine-tune, evaluate, and deploy machine learning and LLM models for production. Create monitoring, evaluation, and feedback tooling, evaluate new techniques, and collaborate cross-functionally to ship ML-powered features.

Develop and productionize consumer-facing AI features using LLMs. Collaborate across the AI stack, run A/B tests and evaluations, improve model reliability and guardrails, and optimize latency, cost, and scalability of production AI systems.