Top Machine Learning Jobs in Boston, MA

Design, build, and support high-throughput, low-latency LLM inference and data pipelines; collaborate with data scientists to post-train and evaluate models; implement scalable, production-ready systems with strong testing, monitoring, and performance focus; mentor engineers and iterate on architecture, reliability, and user-facing AI applications.

Design, develop, deploy, and optimize machine learning models and production services for healthcare products. Build data pipelines, feature workflows, and training datasets; apply testing, validation, MLOps, cloud infrastructure, and monitoring practices. Collaborate with technical and non-technical stakeholders, improve model performance, develop reusable tools and frameworks, evaluate emerging AI technologies, support incident remediation, and provide technical guidance. The role requires production machine learning experience and proficiency in Python, SQL, and Unix environments.

Senior technical leader defining 10-year scientific visions and systems architecture for Bose Research franchises. Ensures coherence across AI/ML, sensing, signal processing, and edge domains; guides foundational IP, models, datasets, and evaluation frameworks. Oversees program architecture, technical governance, and translation of research into scalable platforms and products while representing Bose externally and collaborating with research and engineering stakeholders.

Architect complex embedded hardware and software systems for next-generation audio products. Evaluate feasibility, tradeoffs, scalability, and system constraints across power, compute, memory, latency, performance, and security. Develop simulations, prototypes, AI agents, and automated engineering workflows for architecture exploration and verification. Translate audio, sensing, machine learning, and AI technologies into reusable platforms, frameworks, algorithms, and IP while providing principal-level technical leadership across engineering teams, suppliers, and partners.

Develop autonomy evaluation metrics, statistical and machine-learning analyses, dashboards, and pipelines for simulation and on-road autonomous vehicle testing. Analyze perception, prediction, and planning performance; identify anomalous behavior and critical scenarios; apply VLMs and LLMs where appropriate; and support release gating and safety decisions. The role requires production Python, C++ debugging, technical leadership, cross-functional collaboration, and rigorous software engineering practices.

Leads strategy, roadmap, prioritization, and execution for a shared AI and ML platform. Oversees MLOps, LLM lifecycle management, model evaluation, infrastructure capacity, cost management, observability, and operational readiness. Partners with engineering, finance, infrastructure, vendors, and internal product teams to translate developer needs into platform priorities, manage tradeoffs, improve adoption, and communicate outcomes to executive stakeholders.

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.

Leads AI and machine learning engineering for federal and commercial digital transformation initiatives. Assesses support and contact center environments, designs AI-enabled operating models, develops modernization roadmaps, builds prototypes, creates ROI models, and advises executive stakeholders. The role includes architecture, workshops, client consulting, proposal support, business development, and implementation of conversational AI, automation, virtual agents, LLM orchestration, agentic AI, and workflow optimization solutions.

Leads the applied AI team responsible for ZoomInfo’s B2B data graph, agent memory, production machine learning, LLMs, and agentic systems. Sets technical standards, evaluation methods, cost and latency goals, and model-serving strategy while remaining hands-on with code and prototypes. Hires and develops senior technical talent, guides build-versus-buy and model decisions, and communicates results to executives and cross-functional stakeholders.

Defines enterprise AI and machine learning architectures spanning Generative AI, agentic AI, data, applications, cloud, infrastructure, and enterprise systems. Translates business requirements into architecture blueprints, technical strategies, and implementation roadmaps; develops reusable standards and design patterns; evaluates technology alternatives; and leads technical reviews. The role also establishes patterns for MLOps, model serving, monitoring, governance, security, and deployment across cloud, hybrid, on-premises, and restricted environments while mentoring engineering teams.

Design, build, evaluate, and deploy production AI and ML solutions, including generative AI, large language models, retrieval-augmented generation, and agentic workflows. Develop reliable software, APIs, integrations, evaluations, and observability capabilities while partnering with architecture, platform, data, cybersecurity, and business teams. Apply AI engineering practices across enterprise, cloud, hybrid, and regulated environments.

Build software connecting Fabri’s digital manufacturing process, including manufacturing data collection, process analytics, workflow automation, machine integration, backend services, and internal tools. Develop and deploy machine-learning models for outcome prediction, anomaly detection, inspection, and process monitoring. Analyze process data and geometry, improve manufacturing quality and throughput, and collaborate directly with operators, engineers, and manufacturing teams in an on-site factory environment.

Conduct quantitative research to design and validate trading models, alpha signals, and analytics; apply statistical and ML methods to large financial datasets; develop research code and AI tools (Python, Matlab, SQL); collaborate with traders, SSA researchers, and academics to translate findings into scalable trading indicators and internal/client materials.

Leads development, deployment, and optimization of machine learning, AI, and predictive analytics solutions for U.S. Commercial operations. Builds models using claims, EHR, specialty pharmacy, and commercial data to analyze patient populations, provider behavior, treatment patterns, adherence, and engagement. Partners with Commercial, Medical Affairs, and analytics teams to translate insights into business strategies, while managing vendors, supporting model governance, and communicating findings to leadership.

Lead technical vision and execution for AI/ML, decision systems, and mission planning programs. Manage multidisciplinary teams, ensure schedule and budget adherence, provide technical oversight, cultivate external partnerships, and pursue business growth while guiding program delivery and technical strategy.

Lead Data Scientist will define goals and metrics for algorithm Test & Evaluation, research T&E methodology, lead vendor coordination, present analyses and results to stakeholders, and transition algorithms into software and real-time embedded systems.

Perform data analysis and test & evaluation of machine learning algorithms; develop algorithms for physical and autonomous systems; transition algorithms to software and embedded systems; work with stakeholders to present results and solutions; research new ML and data-analysis methods; handle large, multidimensional datasets and data pipelines.

Develop, test, and integrate AI software for aerospace and industrial applications. Interns will use Python, JavaScript, and C++ to build AI solutions, evaluate data and algorithms, collaborate with machine learning and data engineers, monitor AI trends and regulations, and present progress to leadership and stakeholders.

Validate and independently challenge AI, machine learning, and generative AI models before production deployment. Assess model design, performance, fairness, safety, explainability, robustness, and risk through testing, benchmarking, sensitivity analysis, and quantitative review. Document findings, recommend remediation, support model governance and inventory processes, communicate technical results to diverse stakeholders, and mentor junior data scientists.

Lead research on improving AI agent training data and evaluations. Manage and mentor research engineers, define research direction, design rigorous experiments, validate data quality, diagnose reward and grading failures, and translate findings into scalable tools, workflows, and standards. Collaborate with engineers, domain experts, and vendors while communicating technical conclusions and tradeoffs clearly.

Develop fraud detection models and predictive features for account opening, account takeover, and identity risk. Responsibilities include investigating large datasets, identifying fraud patterns, engineering features, training and evaluating machine learning models, writing tested Python and PySpark code, supporting production deployment, monitoring model performance and drift, and presenting analytical recommendations. The role also requires adherence to data privacy, explainability, model validation, documentation, and governance standards.

Build and evaluate machine learning models and features for fraud detection across account opening, account takeover, and identity risk. Analyze complex datasets to identify fraud patterns, develop hypotheses, validate models, and measure technical and business performance. Write tested Python code, support production deployment and monitoring, communicate findings, and follow standards for privacy, explainability, validation, documentation, and governance.

Senior AI leader driving end-to-end delivery of AI solutions across Corporate Functions, focusing on fraud detection and financial crime prevention. Leads cross-functional teams, defines roadmaps, provides hands-on technical architecture and prototyping, ensures governance/security/compliance, promotes enterprise reuse, and drives stakeholder adoption and measurable business outcomes.

Owns the backlog, sprint cadence, releases, and production health of enterprise AI products. Translates product strategy into requirements, user stories, work packages, and sprint goals while coordinating engineers, data scientists, business partners, and dependent teams. Leads Scrum ceremonies, PI planning, testing, release communications, and uptime monitoring. Applies AI tooling to improve delivery and uses technical fluency to prototype, review pull requests, and support responsible AI implementation.

Lead and grow a team of Conversation Designers to build and scale conversational AI experiences. Partner with Product, Engineering, Analytics, and ML teams to drive strategy, tooling, evaluation, and quality practices. Recruit and remove roadblocks, own strategic initiatives, and remain hands-on in designing, testing, and improving AI-driven conversational products.