Jobgether
Principal Software Engineer, Copilot Code Review
engineeringfull-timeUS
SALARY
$160k – $425k/yr
WORK TYPE
remote
JOB TYPE
full-time
INDUSTRY
general
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About the role
Accountabilities:
- Lead the technical strategy, architecture, design, implementation, and evolution of AI-powered code review systems, remaining deeply hands-on with the most complex engineering challenges.
- Define how the review agent reasons, including the context it uses, models it runs, and information it carries across reviews.
- Evaluate emerging model capabilities and collaborate with research and model-development teams to translate advances in AI into stronger code review experiences.
- Establish and continuously improve the definition of high-quality AI code review through offline evaluations, online experiments, production data, and measurable outcomes.
- Own the end-to-end review experience across development environments, applications, command-line workflows, and pull requests.
- Ensure the reliability, scalability, and operational excellence of a high-throughput distributed service, including participation in an on-call rotation.
- Make complex technical and architectural trade-offs while balancing immediate delivery needs with long-term product and engineering quality.
- Mentor senior and staff engineers, influence technical direction across teams, and contribute to the evolution of engineering practices and workflows.
- Explore new approaches to agentic software development and help evolve engineering workflows to make teams more effective and productive.
- 11+ years of professional experience in software engineering, computer science, or a related technical discipline, with substantial experience delivering and operating production software.
- Equivalent experience may be considered through combinations of education and professional background, including relevant associate, bachelor’s, master’s, or doctoral degrees.
- Strong technical leadership experience, including ownership of architecture, complex technical decisions, and large-scale production systems.
- Extensive programming experience in one or more languages such as C, C++, C#, Java, JavaScript, Go, Ruby, Rust, or Python.
- Proven experience designing and operating scalable distributed systems, including web services, APIs, cloud infrastructure, and modern software engineering practices.
- Hands-on experience applying frontier AI/LLM models at scale, with a practical understanding of their capabilities, limitations, and production considerations.
- Experience building or operating LLM-powered or agentic products in production, or working closely with research teams to bring advanced model capabilities into real-world products.
- Experience designing evaluation frameworks that determine whether model-driven products are improving, including the ability to identify weaknesses in existing evaluation methodologies.
- Strong depth in Go, Ruby, TypeScript, or Python is highly valuable, particularly alongside experience operating distributed services in production.
- Ability to navigate ambiguity, simplify complex technical problems, make sound trade-offs, and deliver high-quality solutions in a fast-paced environment.
- Strong communication, collaboration, mentoring, and influencing skills, with the ability to work effectively across technical and non-technical disciplines.
- Annual base salary range of $160,200–$425,000 USD, depending on factors such as location, experience, knowledge, skills, and qualifications.
- Eligibility for an annual bonus and stock-based rewards for applicable roles.
- Potential additional incentive opportunities depending on role and compensation plan.
- Remote-first working environment within the United States.
- Competitive benefits designed to support employees’ health, wellbeing, and professional development.
- Generous learning and growth opportunities.
- Inclusive, collaborative culture focused on customer impact, continuous improvement, ownership, and innovation.
- Opportunity to work on cutting-edge AI and agentic software development technologies at significant scale.
Requirements:
Benefits:
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