Director, Software Engineering, Machine Learning

Fidelity Financial Services

Confirmed live today High trust

Quick summary

Work type: On-site
Location: Boston, MA
Salary: $126,000–$255,000 / yr
Posted: 3 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $241k

This role $190k

$105k $319k

below market most similar roles pay here above market

This role pays less than 88% of similar roles. Most pay $211,200–$270,500 — the blue band above. At the midpoint, this role pays about $190k versus about $241k for comparable roles.

Based on 240 similar postings.

Employer

About Fidelity Financial Services

Fidelity Investments is one of the largest financial services companies in the world, offering brokerage services, mutual funds, retirement planning, wealth management, and life insurance to individuals and institutions. Industry: Financial Services & Investment Management

Fidelity Financial Services currently has 134 open roles on FindRole.

Listed pay typically runs $107,000–$216,000 across 37 roles with salary data.

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View all roles at Fidelity Financial Services

At a glance

TL;DR · Director, Software Engineering, Machine Learning

Role Posting Log in to save

Director, Software Engineering- Machine Learning (Individual Contributor) joins the Graph Technology team within Data Science to serve as a senior individual contributor. This role involves leading the technical direction for production AI applications, specifically large language model systems, retrieval systems, and knowledge graphs used by quants and portfolio managers for investment decisions. You will own end-to-end architecture for data pipelines, model serving, and production monitoring while setting engineering standards for MLOps and code quality. Key responsibilities include building scalable backend services, APIs, and data pipelines using Python, SQL, and major cloud platforms. Required expertise includes experience with vector databases, graph databases like Neo4j, and orchestration tools like Docker, Kubernetes, and Airflow to deliver well-governed AI in a regulated investment environment.

Skills

Python SQL LLM Generative AI MLOps Knowledge Graphs Vector Databases Neo4j Cypher Docker Kubernetes CI/CD Jenkins GitHub Actions Airflow AWS Azure GCP Streamlit React Vue Graph Neural Networks

What you'll do

Design and lead the development of production LLM, retrieval, and knowledge-graph systems.
Own the end-to-end architecture for data pipelines, model serving, evaluation, and production monitoring.
Set engineering standards for MLOps practices, AI evaluation, and code quality across the team.
Work directly with quants and portfolio managers to define product roadmaps and technical priorities.
Mentor engineers through design reviews, code reviews, and technical guidance.
Evaluate new AI technologies and vendors to make build-vs-buy recommendations for LLM providers and graph platforms.
Collaborate with data engineering, platform, risk, and compliance teams to deliver well-governed AI.

What we're looking for

8+ years in software or machine learning engineering with a track record of leading technical design for production ML products.
Expert Python and strong SQL skills with experience working with large-scale databases.
Hands-on production experience with LLMs, generative AI, information retrieval, and vector databases.
Practical experience with graph databases and knowledge-graph modeling such as Neo4j or Cypher.
Strong experience with CI/CD, containers and orchestration (Docker, Kubernetes), and observability tools.
Experience building scalable backend services, APIs, and data pipelines using major cloud platforms and workflow orchestrators like Airflow.
MLOps experience across the ML lifecycle, including evaluation, performance tuning, and monitoring (preferred).
Advanced degree in Computer Science, Software Engineering, or a related field (preferred).

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