Sysdig is the secure DevOps company, and we are at the forefront of the container, Kubernetes, and cloud revolution. We are passionate, technical problem-solvers, continually innovating and delivering powerful solutions to confidently run cloud-native applications. Our consistent contributions to open source software projects reflect our commitment to the open cloud movement.
We value diversity and open dialog to spur ideas, working closely together to achieve goals. And we are a great place to work too — we were awarded the 2021 Bay Area Best Places to Work Award from San Francisco Business Times and the Silicon Valley Business Journal. We are looking for team members who share our commitment to customers and are willing to dig deeper, understand problems and deliver innovative solutions. Does this sound like the right place for you?
We want to create a Machine Learning based solution that can analyze all activities and events generated across system layers (cloud, syslog, k8s, container, network ...) and automatically detect vulnerabilities and threats.
The Machine Learning engine will adapt to the different client setups (traditional, cloud, container, serverless ...) and process a massive amount of data to define expected and trusted behaviors. All the risky deviations will be alerted and the algorithm will improve the accuracy based on system and admin feedback.
No more static and flaky policies too difficult to tune by a non-specialist, no more noisy alerts that are ignored after some days.
The very last objective is to find “unknown unknowns” and propose prompt remediation (#LI-DNI).
A brilliant Software Engineering student (bachelor’s/master’s degree) who wants to become a software craftsman.
- Continuous learner
- Confident in Linux systems
- Passionate about coding
- Knowledge of major Security threats
- Knowledge of Machine Learning algorithms
- Knowledge of model assessment and selection methods
- Experience coding in Python
- Knowledge about design of experiments and data collection
- Knowledge of deep learning models
- Experience with Tensorflow framework
- Implementation of Machine Learning related solutions (also at the academic level)
- Implementation of Security related solutions (also at the academic level)
What you can expect
An immersive and unforgettable experience
- Duration, up to 6 months (extendable to 12)
- Competitive monthly reimbursement + ticket restaurants
- Flexible working hours (smart-working)
- Supportive team
- Direct supervision from a Senior Machine Learning Engineer
- Concrete possibility of post-internship hiring
Acquired skills post-internship
We plan to cover the full machine learning workflow:
- bibliography research
- data collection
- feature engineering
- model training and assessment
- deployment in production