Machine Learning Jobs in Boston, MA
Machine learning jobs in Boston are concentrated in the Longwood Medical Area, Kendall Square, and the Seaport District, with strong demand in biotech, healthcare AI, and financial technology from employers including Whoop, Wayfair, and SimpliSafe. The city's density of research institutions and venture-backed startups keeps hiring active across ML engineering, NLP, and computer vision roles. See the openings below and apply to the ones that match your experience.
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Showing 5 of 111+ Machine Learning 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 111+ Machine Learning Jobs in Boston
Find roles in Boston that match your experience and apply in just a few clicks.
Find Machine Learning JobsMachine Learning Job Market in Boston
Who's Hiring
- Whoop13
- Wayfair11
- SimpliSafe11
- Zoox6
- Chewy4
Top Industries Hiring
- Technology & Software15
- Automotive6
- Staffing & Recruiting4
- Electronics & Hardware4
- Artificial Intelligence4
Machine Learning Jobs in Boston: Frequently Asked Questions
How do I get a machine learning job in Boston?
Focus your search on the Longwood Medical Area for healthcare AI roles, Kendall Square for biotech and research-driven positions, and the Seaport for fintech and enterprise software companies. Boston employers consistently prioritize candidates with hands-on experience in Python, PyTorch, or TensorFlow and a strong portfolio of applied ML projects. Connections through MIT, Harvard, and Northeastern alumni networks carry real weight in this market.
Which companies hire machine learnings in Boston?
Boston machine learning roles are posted by Whoop, Wayfair, and SimpliSafe and others right now, based on current listings on Migrate Mate as of November 2026. The local employer mix is especially broad, ranging from large healthcare systems and biotech firms to fintech scale-ups and defense contractors clustered around the Route 128 corridor.
Are there remote machine learning jobs in Boston?
Yes, though availability depends on the role. Analytical and modeling-focused positions tend to be more remote-friendly, while roles tied to lab data pipelines or on-site infrastructure are typically in-person. About 45% of machine learning openings tied to Boston are remote or hybrid as of November 2026, with Seaport-based tech and fintech employers offering the most flexible arrangements.
How can I get a machine learning job in Boston with little or no experience?
The most realistic entry path in Boston is through a research assistant or data analyst role at one of the city's academic medical centers or university-affiliated labs, where applied ML work is abundant and mentorship is structured. Boston's biotech and healthtech startups in the Fenway and Cambridge corridors regularly hire junior ML engineers who can demonstrate project work on GitHub. Co-op programs through Northeastern and internships at Kendall Square companies are also a strong on-ramp.
Which industries hire the most machine learnings in Boston?
Boston machine learning roles concentrate in Technology & Software, Automotive, and Staffing & Recruiting, based on current listings on Migrate Mate as of November 2026. Boston's unusual density of world-class hospitals, life sciences companies, and asset management firms creates sustained, specialized demand for ML talent that goes well beyond typical tech-hub hiring patterns.
Related Jobs in Massachusetts
See All 111+ Machine Learning Jobs in Boston
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