Machine Learning Manager Jobs in Boston, MA
Machine Learning Manager jobs in Boston are concentrated in the Seaport, Kendall Square corridor, and Longwood Medical Area, driven by demand across biotech, enterprise software, and financial services. Employers actively hiring include Whoop, Wayfair, and Zoox. Scan the live roles below and apply to whichever ones fit.
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Showing 5 of 89+ Machine Learning Manager jobs
Fidelity will not provide immigration sponsorship for this position.
Job Description:
The Role
Asset Management Technology is hiring a Director, Software Engineering – Machine Learning for its Graph Technology team within Data Science. This is a senior individual contributor role, comparable to Principal Engineer at many firms. It's for a hands-on engineer who wants to set technical direction and lead through influence rather than people management.
You'll be the technical lead for production AI applications, including large language model (LLM) systems, retrieval systems and knowledge graphs, used by quants and portfolio managers to make investment decisions. You'll own the architecture of these systems from start to finish, set engineering standards for the team, and keep writing code. Most of your work will be done directly with investment professionals, turning complex research questions into reliable, scalable tools.
- Design and lead development of production LLM, retrieval and knowledge-graph systems, for investment research and decision support
- Own the end-to-end architecture of multiple ML applications: data pipelines, model serving, evaluation and production monitoring
- Set engineering standards for AI evaluation, MLOps practices and code quality, and drive their adoption across the team
- Work directly with quants and portfolio managers to define problems, decide priorities and shape the product roadmap, deciding what to build as well as how
- Mentor engineers at every level and raise the bar through design reviews, code reviews and technical guidance
- Work across teams, including data engineering, platform, risk and compliance, to deliver well-governed AI in a regulated environment
- Evaluate new AI technologies and vendors and make build-vs-buy recommendations on LLM providers, graph platforms and tools
The Expertise and Skills You Bring
Required
- 8+ years in software or machine learning engineering, with a track record of leading the technical design of ML products shipped to production
- Expert Python and strong SQL, with experience working with large-scale databases
- Strong experience building scalable backend services and APIs to serve ML models
- Hands-on production experience with LLMs and generative AI, information retrieval and vector databases
- Practical experience with graph databases and knowledge-graph modeling (e.g., Neo4j, Cypher)
- DevOps: Strong experience with CI/CD (e.g., Jenkins, GitHub Actions), containers and orchestration (Docker, Kubernetes), and observability (logging, metrics, alerting)
- Experience with a major cloud platform (AWS, Azure or GCP) and its ML services
- Experience building data pipelines and orchestrating workflows (e.g., Airflow)
- Strong software engineering practices: testing, code review, maintainable design and agile delivery
- Technical influence beyond your own team: mentoring, setting standards or driving architecture decisions
- Can explain complex models, results and trade-offs clearly to non-technical and investment audiences
Preferred
- MLOps: Deep experience across the ML lifecycle in production, including model and LLM evaluation, performance tuning, model and prompt versioning, and monitoring for quality, drift, latency and cost
- Experience with investment data (market data, fundamentals, holdings, alternative data) or buy-side workflows
- Experience with LLM guardrails, reducing hallucinations, and model risk management or AI governance
- Background in graph ML (e.g., graph neural networks), entity resolution or ontology design
- Experience with enterprise job schedulers (e.g., Autosys)
- Familiarity with front-end tools for fast prototyping (e.g., Streamlit, React, Vue)
- Advanced degree in Computer Science, Software Engineering or a related field (equivalent experience is equally valued)
The Team
The Graph Technology team is part of Quant Technology within Fidelity's Asset Management Technology organization. We build scalable investment tools that give our investment teams an edge, from knowledge graphs that connect companies, securities and events to LLM-powered research tools that put insight in front of portfolio managers faster. Our work directly supports Asset Management's investment performance and its goals for scale and efficiency.
Fidelity’s Onsite Working Model
Fidelity is transitioning to a full-time onsite working model through a phased rollout across regions and roles. Currently, some roles and locations require 100% onsite presence, while others require less. Onsite expectations are likely to evolve as the rollout continues. This transition does not apply to fully remote roles.
Placement in the range will vary based on job responsibilities and scope, geographic location, candidate’s relevant experience, and other factors.
Base salary is only part of the total compensation package. Depending on the position and eligibility requirements, the offer package may also include bonus or other variable compensation.
We offer a wide range of benefits to meet your evolving needs and help you live your best life at work and at home. These benefits include comprehensive health care coverage and emotional well-being support, market-leading retirement, generous paid time off and parental leave, charitable giving employee match program, and educational assistance including student loan repayment, tuition reimbursement, and learning resources to develop your career. Note, the application window closes when the position is filled or unposted.
Please be advised that Fidelity’s business is governed by the provisions of the Securities Exchange Act of 1934, the Investment Advisers Act of 1940, the Investment Company Act of 1940, ERISA, numerous state laws governing securities, investment and retirement-related financial activities and the rules and regulations of numerous self-regulatory organizations, including FINRA, among others. Those laws and regulations may restrict Fidelity from hiring and/or associating with individuals with certain Criminal Histories.
Certifications:
Category:
Information TechnologyFidelity will not provide immigration sponsorship for this position.
Job Description:
The Role
Asset Management Technology is hiring a Director, Software Engineering – Machine Learning for its Graph Technology team within Data Science. This is a senior individual contributor role, comparable to Principal Engineer at many firms. It's for a hands-on engineer who wants to set technical direction and lead through influence rather than people management.
You'll be the technical lead for production AI applications, including large language model (LLM) systems, retrieval systems and knowledge graphs, used by quants and portfolio managers to make investment decisions. You'll own the architecture of these systems from start to finish, set engineering standards for the team, and keep writing code. Most of your work will be done directly with investment professionals, turning complex research questions into reliable, scalable tools.
- Design and lead development of production LLM, retrieval and knowledge-graph systems, for investment research and decision support
- Own the end-to-end architecture of multiple ML applications: data pipelines, model serving, evaluation and production monitoring
- Set engineering standards for AI evaluation, MLOps practices and code quality, and drive their adoption across the team
- Work directly with quants and portfolio managers to define problems, decide priorities and shape the product roadmap, deciding what to build as well as how
- Mentor engineers at every level and raise the bar through design reviews, code reviews and technical guidance
- Work across teams, including data engineering, platform, risk and compliance, to deliver well-governed AI in a regulated environment
- Evaluate new AI technologies and vendors and make build-vs-buy recommendations on LLM providers, graph platforms and tools
The Expertise and Skills You Bring
Required
- 8+ years in software or machine learning engineering, with a track record of leading the technical design of ML products shipped to production
- Expert Python and strong SQL, with experience working with large-scale databases
- Strong experience building scalable backend services and APIs to serve ML models
- Hands-on production experience with LLMs and generative AI, information retrieval and vector databases
- Practical experience with graph databases and knowledge-graph modeling (e.g., Neo4j, Cypher)
- DevOps: Strong experience with CI/CD (e.g., Jenkins, GitHub Actions), containers and orchestration (Docker, Kubernetes), and observability (logging, metrics, alerting)
- Experience with a major cloud platform (AWS, Azure or GCP) and its ML services
- Experience building data pipelines and orchestrating workflows (e.g., Airflow)
- Strong software engineering practices: testing, code review, maintainable design and agile delivery
- Technical influence beyond your own team: mentoring, setting standards or driving architecture decisions
- Can explain complex models, results and trade-offs clearly to non-technical and investment audiences
Preferred
- MLOps: Deep experience across the ML lifecycle in production, including model and LLM evaluation, performance tuning, model and prompt versioning, and monitoring for quality, drift, latency and cost
- Experience with investment data (market data, fundamentals, holdings, alternative data) or buy-side workflows
- Experience with LLM guardrails, reducing hallucinations, and model risk management or AI governance
- Background in graph ML (e.g., graph neural networks), entity resolution or ontology design
- Experience with enterprise job schedulers (e.g., Autosys)
- Familiarity with front-end tools for fast prototyping (e.g., Streamlit, React, Vue)
- Advanced degree in Computer Science, Software Engineering or a related field (equivalent experience is equally valued)
The Team
The Graph Technology team is part of Quant Technology within Fidelity's Asset Management Technology organization. We build scalable investment tools that give our investment teams an edge, from knowledge graphs that connect companies, securities and events to LLM-powered research tools that put insight in front of portfolio managers faster. Our work directly supports Asset Management's investment performance and its goals for scale and efficiency.
Fidelity’s Onsite Working Model
Fidelity is transitioning to a full-time onsite working model through a phased rollout across regions and roles. Currently, some roles and locations require 100% onsite presence, while others require less. Onsite expectations are likely to evolve as the rollout continues. This transition does not apply to fully remote roles.
Placement in the range will vary based on job responsibilities and scope, geographic location, candidate’s relevant experience, and other factors.
Base salary is only part of the total compensation package. Depending on the position and eligibility requirements, the offer package may also include bonus or other variable compensation.
We offer a wide range of benefits to meet your evolving needs and help you live your best life at work and at home. These benefits include comprehensive health care coverage and emotional well-being support, market-leading retirement, generous paid time off and parental leave, charitable giving employee match program, and educational assistance including student loan repayment, tuition reimbursement, and learning resources to develop your career. Note, the application window closes when the position is filled or unposted.
Please be advised that Fidelity’s business is governed by the provisions of the Securities Exchange Act of 1934, the Investment Advisers Act of 1940, the Investment Company Act of 1940, ERISA, numerous state laws governing securities, investment and retirement-related financial activities and the rules and regulations of numerous self-regulatory organizations, including FINRA, among others. Those laws and regulations may restrict Fidelity from hiring and/or associating with individuals with certain Criminal Histories.
Certifications:
Category:
Information TechnologySee All 89 Machine Learning Manager Jobs in Boston
Find roles in Boston that match your experience and apply in just a few clicks.
Find Machine Learning Manager JobsMachine Learning Manager Job Market in Boston
Who's Hiring
- Whoop10
- Wayfair10
- Zoox6
- SimpliSafe6
- Chewy4
Top Industries Hiring
- Technology & Software12
- Automotive6
- Artificial Intelligence4
- Investment & Asset Management4
- Trucking2
Machine Learning Manager Jobs in Boston: Frequently Asked Questions
How do I get a machine learning manager job in Boston?
Focus your search on Boston's biotech corridor around Longwood and Kendall Square, enterprise software firms in the Seaport, and fintech companies downtown. Candidates who stand out in this market typically combine hands-on ML engineering experience with demonstrated team leadership and cross-functional product collaboration. Publishing research, contributing to open-source projects, or holding an advanced degree from one of Boston's many research universities gives you a concrete edge with local hiring managers.
Which companies hire machine learning managers in Boston?
Companies currently hiring machine learning managers in Boston include Whoop, Wayfair, and Zoox, per current listings on Migrate Mate as of November 2026. Boston's hiring base is broad, ranging from large academic medical centers and life sciences firms to venture-backed AI startups and established financial technology companies.
Are there remote machine learning manager jobs in Boston?
Yes, though availability depends on the role's scope, since positions requiring direct oversight of lab infrastructure or on-site model deployment tend to be in-person. About 43% of machine learning manager openings tied to Boston are remote or hybrid as of November 2026. Strategy-heavy and platform-focused roles at Boston's software and fintech employers are most likely to offer remote flexibility.
How can I get a machine learning manager job in Boston with little or no experience?
The most realistic path is moving into a senior ML engineer or tech lead role first, particularly at one of Boston's many mid-stage startups or academic spinouts in the Seaport and Cambridge where team structures are flat and leadership opportunities appear early. Entry-level candidates often transition through roles at biotech companies or university-affiliated research labs, where managing small cross-functional projects builds the management record Boston hiring managers look for before promoting or hiring into a full manager title.
Which industries hire the most machine learning managers in Boston?
Boston machine learning manager roles concentrate in Technology & Software, Automotive, and Artificial Intelligence, based on current listings on Migrate Mate as of November 2026. Boston's dense cluster of research hospitals, life sciences companies, and fintech headquarters creates sustained demand that sets it apart from other U.S. tech markets.
Related Jobs in Massachusetts
See All 89 Machine Learning Manager Jobs in Boston
Find roles in Boston that match your experience and apply in just a few clicks.
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