Senior Machine Learning Engineer
Senior Machine Learning Engineer
Employer Active
Job Alert
You will be updated with latest job alerts via email
Share
Job Alert
You will be updated with latest job alerts via email
Job Description
About GitGuardian
GitGuardian is a global post-Series B cybersecurity scale-up.
Among our early investors who saw our market value proposition are the co-founder of GitHub Scott Chacon along with Solomon Hykes Dockers co-founder. American and European top-tier VC firms have also invested in GitGuardian.
GitGuardian leads the way in Non-Human Identity security offering end-to-end solutions from secrets detection in code productivity tools and environments to strong remediation observability and proactive prevention of leaks. Our solutions are already used by more than 600K developers worldwide!
About your team and your mission
GitGuardian is a tech company so engineering sits at the heart of everything we do. The department is working on solving challenging problems:
- Scanning various data streams at scale to find secrets in them (scanning >10M code patches messages or images daily)
- Developing components that are deployed on our customers infrastructure to securely collect and map non-human identities
- Training and deploying models and algorithms to surface aggregate and contextualize rich metadata around each secret then integrating those insights into the product without compromising user experience.
Youll join our Machine Learning squada team of four engineers within our 50-strong engineering departmentworking together to build and ship ML features for our products.
Today our priority is helping SecOps who are using GitGuardian to prioritize and navigate incidents. Some incidents if abused can cause hundreds of millions of dollars in damage.
We deeply believe machine learning is essential to building an effective prioritization algorithm and that this algorithm must leverage all available contextfrom information in the patch and repository to company-level and asset-level data. This is why we work closely with both the Secret Detection team in charge of our secret detection engine and the Incidents team who owns the interface and incidents management in the app.
Your daily responsibilities will be to:
- Write code daily to make our platform smarter faster and more reliable.
- Train evaluate and iterate on models using our large multi-modal dataset
- Drive end-to-end ML/AI projects from scoping and prototyping through deployment and monitoring
- Level up our MLOps deployment for larger models at the scale we have and with the additional complexity of self hosted compatibility.
- Bring expertise and best practices: define conventions review code and mentor junior engineers.
- Contribute to the continuous improvement of our existing deployment pipelines optimizing inference speed and any other ideas to improve our day to day and reliability.
Technical environment
- Languages & frameworks: Python PyTorch/Transformers ONNX Runtime BentoML scikit-learn LiteLLM
- Data & orchestration: DVC SkyPilot Snowflake Dagster
- Main Application: Celery Django PostgreSQL Redis
- Infrastructure & Deployment: AWS Kubernetes ArgoCD Gitlab
- Collaboration: Slack Linear Notion
More details on our current stack here!
What makes this position unique
GitGuardian is a tech oriented company with a mission: making the world safer for developers. Thanks to very talented engineers we are selling a strong product to top level companies that have a high level of expectations. As a data driven company from day one GitGuardian has more than 40B code patches in our DBs and weve been running our models at scale on a huge volume of data for years now!
About you
If you think you match at least 70% of these criteria please apply!
We are looking for a Senior ML Engineer with strong ML Ops and Software Engineering skills. Heres what we consider essential for success in this role:
- You have a fluent English & French level being able to express ideas to engineers or non-tech stakeholders
- You have experience shipping models in production (5 years as an ML Engineer)
- You master core ML skills: PyTorch Transformers scikit-learn designing custom training pipelines.
- You are seasoned with the following ML Ops skills:
- Experimentation Environment: DVC SkyPilot Dagster (or equivalent).
- Model deployment: ONNX Runtime BentoML (or equivalent) in cloud-native environments.
- Infra & tooling: AWS Kubernetes/ArgoCD GitLab CI/CD Docker.
- Monitoring & reliability: Grafana Sentry (or similar) for production ML.
- You focus on building reusable and maintainable systems thanks to pragmatic planning balancing quick wins with a long-term vision.
The following skills would strengthen your application but arent required:
- Having deployed LLMs or agent-based systems at scale.
- Having domain experience in cybersecurity/secrets detection.
- Being familiar with PostgreSQL Django Celery.
- Having built or maintained self-hosted/on-prem ML deployments.
The interview process
At GitGuardian we are committed to building a diverse equitable and inclusive workforce.
We will ask for your gender identity on the application page to help us understand the diversity of our applicant pool and to track our progress in attracting and hiring a diverse workforce. The information is optional and will not be disclosed to the hiring manager or the interview team and will not be considered in the hiring process. We appreciate your willingness to share this with us so that we can continue to improve our diversity equity and inclusion efforts.
1. Video call with a Talent Acquisition team member
To discover your professional project and evaluate if there could be a mutual match.
2. Technical interview with Engineers (1h30)
To evaluate your skills for the position and project yourself into the role.
Live coding & ML system-design: model training infra monitoring trade-offs.
3. Interview with your future manager
To know more about yourself your achievements and present to you the team.
Deep dive on past projects career goals team fit.
4. Final interview with a Senior Engineering Manager
To detail our companys vision and ambitions for the next couple of years.
Benefits
- Package that includes stock-options
- Lunch voucher (Swile)
- Non-charged health insurance for children (Sidecare / Generali)
- Up to 300 to improve your home office set-up
- Yearly holiday allowance
- Referral bonus of 4000 for any new Guardian we might hire thanks to you
- Team building: monthly budget dedicated to each employee that you can spend as you wish with colleagues (latest examples to date: star restaurant karaoke stand-up show karting ...)
- Pet-friendly offices some Guardians gets to bring their dogs from time to time
And also...
- Remote policy: hybrid (2 days/week at the office) or full-remote (but 3 days/month at the office)
- Opportunities for career development in the long term
- A robust engineering culture discover our R&D projects
More about GitGuardian!
Products
- Understand how GitGuardian works in this short video!
- Want to go even further Check out our public roadmap!
- Check out the State of Secrets Sprawl Report to understand our mission and the industry.
- Our solutions are already used by hundreds of thousands of developers in all industries and GitGuardian platform is the n1 app on the GitHub marketplace
Clients
- GitGuardian helps organizations find exposed sensitive information that could often lead to tens of millions of dollars in potential damage.
- More than 70% of our customers are in the United States.
- Many F500 companies use GitGuardians platform.
People
- The Guardians are knowledgeable committed serious aligned with the companys mission and true team players: always willing to help each other grow our skill sets!
- The team is diverse and we hail from more than 20 different countries.
- We are also agile remote-friendly and fun people to work with.
- You will get trust & autonomy on your perimeter with a very transparent internal communication and a strong impact on the company development.
Employment Type
Full Time
