Context

• Our products are a set of tools that scan GitHub public activity and git private repositories for security vulnerabilities.

• They are used by different teams: Software Development and Ops teams, Application Security, Threat Response and the buying decision comes from CISOs / CTOs / Directors of Security.

• By design GitGuardian is a data driven company. Both co-founders are former Data Scientists and the first product of GitGuardian is real-time processing of all new GitHub events. Our secret detection engine has been battle tested against huge amounts of data.

In this context, GitGuardian now wants to take it to the next level by incorporating Machine Learning models to create better vulnerability detectors and also improve internal performance efficiency. That’s why your work will matter and will be taken seriously !

Missions

As a Machine Learning Engineer, you will have to:

• Lead the end-to-end development of scalable and reliable Machine Learning models that can be used to solve business problems. For instance, build a new generation of secret detectors using state-of-the-art LLMs.

• Identify areas in the company where Machine Learning can be applied. In particular, launch ML experiments to bring new in-app features to our existing products, like incident severity classification.

• Deploy tools to monitor the quality and performance of the models, while ensuring that they meet the business requirements

• Work closely with the Data Engineering team to ensure smooth data integration

• Collaborate with DevOps and Software Engineering teams to deploy models into our products, monitor their performance, and troubleshoot issues as needed

• Stay up-to-date with the latest advancements in NLP and ML technologies to implement new techniques into the existing models and foster a culture of innovation and continuous learning

• Communicate about cutting-edge ML applications at GitGuardian by writing blog posts, participating in meetups

Advantages

• You will build and deploy state-of-the-art models that bring high value to the business

• You will be able to leverage a huge amount of textual data collected from day 1

• The ML tooling landscape is still to be defined

• You will be part of a scale-up adventure with a strong engineering culture

Our technical stack

• Snowflake

• PostgreSQL, Elasticsearch, MongoDB

• Airbyte

• Metabase, Tableau

• GitLab

• AWS, Terraform, Docker, Kubernetes

_If you think you are only matching 70% to 80% of these criterias, please send us your resume !
And if you still have some questions before applying, you can directly write to us at :_ 

careers@gitguardian.com 

Hard skills

• 5+ years of hands-on experience in building, deploying and maintaining ML models with concrete business applications

• Solid analytical and advanced statistical skills

• Deep knowledge of state-of-the-art NLP techniques and models, especially LLMs

• Strong programming skills in one or more programming languages focused on data processing (Python, Scala, etc.) along with skills in application best practices (code modularity, unit tests, documentation, etc.)

• Fluent in ML libraries such as PyTorch, Transformers, spaCy, scikit-learn

• Strong experience in packaging and delivering ML models in production using cloud-based platforms

• Experience with Docker and MLOps tools (Airflow, MLFlow)

• Experience in using Hugging Face and transformers is a plus

• Experience in Data Warehousing (Snowflake, BigQuery) and data app prototyping (Streamlit, Dash) is a plus

Soft skills

• You like algorithms and new technology

• You like to write high quality and re-usable code

• You are used to perform applied research projects and bring them to production

• You are autonomous, proactive and curious

• You are a team player with strong communication skills. In particular, you should be able to work with cross-functional teams, and be able to communicate technical concepts to non-technical stakeholders.

• You are able to work in a fast-paced and dynamic environment, and adapt to changing requirements

• You speak fluent French and English

Bonus points

• You don’t embed API keys in your code ;)

• Deep understanding of the startups dynamics and challenges

• Have experienced strong team growth in a previous company

1 visio call with a recruiter

To discover your professional project, present to you the team, and evaluate if there could be a mutual match

1 technical team interview

To evaluate your hard skills for the position and project yourself into the role

1 technical test depending on your seniority

To see how you are doing hands on coding

1 final interview with the CEO and co-founder

To explain to you our company’s vision and ambitions to the next couple of years, and make sure you are up for the position