Machine Learning Engineering roles

Machine Learning (ML) Engineers at GitLab develop and implement models to support and enable experimental, beta, and generally available AI features in GitLab.

Engineers work as part of a team in the Data Science section, and collaborate with counterparts across Product, Engineering, UX and Data, and work closely with Backend, Frontend, and Fullstack engineers to integrate their work into GitLab.

Levels

Associate Machine Learning Engineer

The Associate level in the Engineering Division is a grade 5.

Responsibilities

Develop improvements to models to generate new content using machine learning models in a secure, well-tested, and performant way.
Work with highly complex data for feature development using machine learning models.
Collaborate with product managers, engineers, and other stakeholders as a machine learning specialist.
Advocate for improvements to product quality, security, and performance.
Solve technical problems of moderate scope and complexity.
Craft code that meets our internal standards for style, maintainability, and best practices for a high-scale machine learning environment. Maintain and advocate for these standards through code review.
Confidently ship small features and improvements with guidance and support from other team members.

Requirements

1 or more years of experience in ML, or a Master’s or PhD degree with a focus on Machine Learning
Demonstrated capacity to develop and implement deep learning models
Experience with Python
Comfort working in a highly agile, intensely iterative software development process
Positive and solution-oriented mindset
Effective communication skills: Regularly achieve consensus with peers, and clear status updates
An inclination towards communication, inclusion, and visibility
Self-motivated and self-managing, with strong organizational skills.
Ability to work closely with other parts of the organization
Share our values, and work in accordance with those values
Ability to thrive in a fully remote organization
Ability to use GitLab

Machine Learning Engineer Intermediate

The Intermediate level in the Engineering Division is a grade 6.

Responsibilities

Develop improvements to models to generate new content using machine learning models in a secure, well-tested, and performant way.
Work with highly complex data for feature development using machine learning models.
Collaborate with product managers, engineers, and other stakeholders as a machine learning specialist.
Advocate for improvements to product quality, security, and performance.
Solve technical problems of moderate scope and complexity.
Craft code that meets our internal standards for style, maintainability, and best practices for a high-scale machine learning environment. Maintain and advocate for these standards through code review.
Confidently ship small features and improvements with minimal guidance and support from other team members. Collaborate with the team on larger projects.
Participate as a reviewer or project maintainer in one or more engineering projects. For more information regarding timelines and exceptions, see this page.
Participate in Tier 2 or Tier 3 weekday and weekend and occasional night on-call rotations to assist troubleshooting product operations, security operations, and urgent engineering issues.

Requirements

2 or more years of experience in ML, or a Master’s or PhD degree with a focus on Machine Learning
Experience developing and implementing deep learning models
Professional experience with Python
Experience with performance and optimization problems and a demonstrated ability to both diagnose and prevent these problems
Comfort working in a highly agile, intensely iterative software development process
Demonstrated ability to onboard and integrate with an organization long-term
Positive and solution-oriented mindset
Effective communication skills: Regularly achieve consensus with peers, and clear status updates
An inclination towards communication, inclusion, and visibility
Experience owning a project from concept to production, including proposal, discussion, and execution.
Self-motivated and self-managing, with strong organizational skills.
Demonstrated ability to work closely with other parts of the organization
Share our values, and work in accordance with those values
Ability to thrive in a fully remote organization
Ability to use GitLab
Comfort and familiarity with our code review process

Senior Machine Learning Engineer

The Senior level in the Engineering Division is a grade 7.

Senior Responsibilities

Design and develop models to generate new content using machine learning models in a secure, well-tested, and performant way.
Analyze and interpret highly complex data to arrive at actionable recommendations for feature development using machine learning models.
Collaborate with product managers, engineers, and other stakeholders as a specialist and subject matter expert in machine learning.
Advocate for improvements to product quality, security, and performance that have particular impact across your team.
Solve technical problems of high scope and complexity.
Exert influence on the overall objectives and long-range goals of your team.
Help to define and improve our internal standards for style, maintainability, and best practices for a high-scale machine learning environment. Maintain and advocate for these standards through code review.
Confidently ship moderately sized features and improvements with minimal guidance and support from other team members. Collaborate with the team on larger projects.
Improves the engineering projects at GitLab via the maintainer program at own comfortable pace, while striving to become a project maintainer.
Participate in Tier 2 or Tier 3 weekday and weekend and occasional night on-call rotations to assist troubleshooting product operations, security operations, and urgent engineering issues.

Senior Requirements

The following extend the main requirements for this role.

3 or more years of experience in ML, or a Master’s or PhD degree with a focus on Machine Learning
Demonstrated experience developing and implementing deep learning models
Significant professional experience with Python
Experience with performance and optimization problems for Machine Learning models and a demonstrated ability to both diagnose and prevent these problems

Staff Machine Learning Engineer

The Staff level in the Engineering Division is a grade 8.

Staff Responsibilities

Design and develop models to generate new content using machine learning models in a secure, well-tested, and performant way.
Analyze and interpret highly complex data to arrive at actionable recommendations for feature development using machine learning models.
Collaborate with product managers, engineers, and other stakeholders as a specialist and subject matter expert in machine learning.
Advocate for improvements to product quality, security, and performance that have particular impact across your team and others.
Solve technical problems of the highest scope and complexity for your team.
Exert influence on the overall objectives and long-range goals of your team.
Shepherd the definition and improvement of our internal standards for style, maintainability, and best practices for a high-scale machine learning environment. Maintain and advocate for these standards through code review.
Drive innovation on the team with a willingness to experiment and to boldly confront problems of immense complexity and scope.
Actively seek out difficult impediments to our efficiency as a team (“technical debt”), propose and implement solutions that will enable the entire team to iterate faster.
Represent GitLab and its values in public communication around broad initiatives, specific projects, and community contributions. Interact with customers and other external stakeholders as a consultant and spokesperson for the work of your team.
Provide mentorship for all Engineers on your team to help them grow in their technical responsibilities and remove blockers to their autonomy.
Confidently ship large features and improvements with minimal guidance and support from other team members. Collaborate with the team on larger projects.
Improves the engineering projects at GitLab via the maintainer program at own comfortable pace, while striving to become a project maintainer.
Participate in Tier 2 or Tier 3 weekday and weekend and occasional night on-call rotations to assist troubleshooting product operations, security operations, and urgent engineering issues.

Staff Requirements

The following extend the main requirements for this role.

5 or more years of experience in ML, or a PhD degree with a focus on Machine Learning
Significant experience developing and implementing deep learning models
Significant professional experience with Python in a Machine Learning capacity
Experience with performance and optimization problems for Machine Learning models and a demonstrated ability to both diagnose and prevent these problems
Demonstrated capacity to clearly and concisely communicate about complex technical, architectural, and/or organizational problems and propose thorough iterative solutions

Engineering Manager Machine Learning

The Engineering Manager in the Engineering Division is a grade 8.

Engineering Manager Machine Learning Responsibilites

Leverage their knowledge to lead teams of machine learning engineers and other engineers to tune, train, and create unique ML models, test 3rd party models using decision science, and create/maintain components to apply machine learning to the GitLab product
Lead teams at GitLab develop and implement models to support and enable experimental, beta, and generally available AI features in GitLab.
Additional engineering manager responsibilities

Engineering Manager Machine Learning Requirements

5 or more years of experience in ML, or a PhD degree with a focus on Machine Learning

Standard Engineering Manager requirements including

Ability to use GitLab
Exquisite brokering skills: regularly achieve consensus amongst departments
5 years or more experience
2 years or more experience in a leadership role with current technical experience
In-depth technical experience in at least one of the core languages, frameworks, or technologies used by your team
Familiarity with other functional areas of your team
Any additional experience required by the position’s specialty
Excellent written and verbal communication skills
You share our values, and work in accordance with those values
Demonstrated experience leading teams that are developing and implementing deep learning models
Expert level experience with Python
Expert experience with performance and optimization problems for Machine Learning models and a demonstrated ability to both diagnose and prevent these problems
Ability to map and communicate business requirements to facilitate their implementation in machine learning initiatives

Performance Indicators

Machine Learning Engineers have the following job-family performance indicators.

Machine Learrning Managers have the same engineering manager performance indicators as other engineering managers.

Career Ladder

For more details on the engineering career ladders, please review the engineering career development handbook page.

Hiring Process

Candidates for this position can expect the hiring process to follow the order below. Please keep in mind that candidates can be declined from the position at any stage of the process.

Qualified candidates will be invited to schedule a 30 minute screening call with one of our Global Recruiters.

For Individual Contributors:

As part of the application, candidates are asked to complete a short technical questionnaire, with a possibility of additional technical questions being asked if needed after the application is submitted.
Next, candidates will be invited to schedule a 30 minute screening call with one of our Technical Recruiters
Next, candidates will be invited to schedule a 90 minute technical interview with one of our Backend Engineers
Next, candidates will be invited to schedule a 60 minute interview with one of our Backend Engineering Managers
Next, candidates will be invited to schedule a 60 minute interview with our Director of Engineering
Successful candidates will subsequently be made an offer. Additional details about our process can be found on our hiring page.

For Managers:

Selected candidates will be invited to schedule a 30 minute screening call with one of our Technical Recruiters
Next, candidates will be invited to schedule a 60 minute first interview with a Director of Engineering
Next, candidates will be invited to schedule a 45 minute second peer interview with an Engineering Manager
Next, candidates will be invited to schedule a 45 minute third interview with another member of the Engineering team
Next, candidates will be invited to schedule a 45 minute fourth interview with a member of the Product team
Next, candidates will be invited to schedule a 45 minute fifth interview with our VP of Engineering
Finally, candidates may be asked to schedule a 50 minute final interview with our CEO
Successful candidates will subsequently be made an offer via email

Additional details about our process can be found on our hiring page.

About GitLab

GitLab Inc. is a company based on the GitLab open-source project. GitLab is a community project to which over 2,200 people worldwide have contributed. We are an active participant in this community, trying to serve its needs and lead by example. We have one vision: everyone can contribute to all digital content, and our mission is to change all creative work from read-only to read-write so that everyone can contribute.

We value results, transparency, sharing, freedom, efficiency, self-learning, frugality, collaboration, directness, kindness, diversity, inclusion and belonging, boring solutions, and quirkiness. If these values match your personality, work ethic, and personal goals, we encourage you to visit our primer to learn more. Open source is our culture, our way of life, our story, and what makes us truly unique.

Top 10 Reasons to Work for GitLab:

Mission: Everyone can contribute
Results: Fast growth, ambitious vision
Flexible Work Hours: Plan your day so you are there for other people & have time for personal interests
Transparency: Over 2,000 webpages in GitLab handbook, GitLab Unfiltered YouTube channel
Iteration: Empower people to be effective & have an impact, Merge Request rate, We dogfood our own product, Directly responsible individuals
Diversity, Inclusion & Belonging: A focus on gender parity, Team Member Resource Groups, other initiatives
Collaboration: Kindness, saying thanks, intentionally organize informal communication, no ego
Total Rewards: Competitive market rates for compensation, Equity compensation, global benefits (inclusive of office equipment)
Work/Life Harmony: Flexible workday, Family and Friends days
Remote Done Right: One of the world's largest all-remote companies, prolific inventor of remote best practices

See our culture page for more!

Work remotely from anywhere in the world. Curious to see what that looks like? Check out our remote manifesto and guides.

Last modified September 23, 2024: Fix broken links (d748cf8c)

View page source - Edit this page - please contribute.