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Machine Learning Engineer

Git*** · United States
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United States107,700-285,900 USD/yearlyRemote
Remuneration
107,700-285,900 USD/yearly
Location
United States
Visa sponsorship
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Job summary

- United States - Security - Experienced Professional - Individual Contributor - Yes - 5507 JOB DESCRIPTION About Git*** Git*** is the world’s leading platform for agentic software development — powered by Copilot to build, scale, and deliver secure software.

Benefits

And additional rewards, including annual bonus and stock.These rewards are allocated based on individual impact in role.This position will be open for a minimum of 3 days, with applications accepted oGitHub valuesCustomer-obsessedShip to learnGrowth mindsetOwn the outcomeBetter togetherDiverse and inclusiveManager fundamentalsModel

Qualifications

  • Experienced Professional
  • We're looking for an experienced machine learning engineer to help design, build and deploy agentic solutions, and to conduct ad-hoc analysis, as you help protect the home of all developers.
  • Required
  • 4+ years experience in machine learning, or related field
  • OR Bachelor's Degree in Computer Science, Software Development, Electrical or Computer Engineering, Mathematical Sciences, or related field AND 2+ years experience in machine learning, or related field
  • OR Master's Degree in Machine Learning, Computer Science, Software Development, Electrical or Computer Engineering, Mathematical Sciences, or related field
  • OR equivalent experience.
  • Preferred
  • Strong understanding of large language models — how they work — and hands-on experience applying them at scale, ideally for classification, agentic workflows, or agents.
  • Strong software engineering

Responsibilities

  • Build well-engineered, production-grade systems that run reliably against high-volume event streams, making effective use of AI coding assistants to accelerate and improve your work.
  • Build and operate scalable ML systems on cloud platforms (such as Azure AI Foundry) for training, deploying, and serving models and agentic solutions in production.
  • Evaluate and improve existing models and agentic solutions using offline evaluations (including tool-use loops and LLM-as-judge evaluation), performance metrics, and feedback from operational deployments.
  • Identify vulnerabilities in products that lead to abuse, and provide consultation to product teams reviewing new features.
  • Collaborate closely with cross-functional teams including data scientists, software engineers, product managers and content moderators to integrate agentic solutions into production systems.
  • Document the systems you help build and support the technical growth of your peers.

Skills

Leadership

Degrees

AssociateBachelorDegreeMaster

Industry

Energy

Company size

Smb