At Cardiologs, our mission is to democratize access to expert cardiac care through medical-grade artificial intelligence and cloud technology(e)Our flagship product, the Cardiologs platform, is a leading software for the analysis of ambulatory ECG used to help diagnose over 1 million patients per year(e)More recently, we launched a Remote Patient Monitoring solution in the US and in Europe, allowing patients to share their smartwatch ECGs with their physicians, who in turn use our cloud-based RPM platform to streamline ECG reviews.
We have an innovative, result-oriented work environment where we foster ambition and dedication while being mindful of our teammates’ work and achievements(e)We also nurture our company culture with monthly all-hands meetings and apéros, weekly yoga classes, and other fun activities.
As we continue to grow, we are looking for a Staff Machine Learning Engineer to join the team in Paris.
What you’ll do
We are looking for an experienced machine learning (ML) engineer to own the strategy behind Cardiologs' environment for ML development(e)You will ensure continuous training of ML models and tracking of their performance(e)You will also set up or develop tools to facilitate the development by other teams of algorithms that are used to streamline the diagnosis of several thousands of patients daily.
You will:
- set up continuous training of deep learning models at scale across multiple machines;
- develop dataset management tools to allow continuous improvement and ensure reproducibility;
- improve evaluations of current and future machine learning models to ensure non-regression and track improvements;
- set up existing tools, or develop internal ones to facilitate fast and reliable ML pipeline development by other teams;
- and optimize ML pipelines in production in terms of speed and reliability.
You will also :
- build your team roadmaps in consensus with other stakeholders;
- set a nice work environment for your team, in line with our values;
- stay up-to-date with data engineering industry standards;
- and participate in knowledge-sharing events with the rest of the research team.
Success in this role also requires a good understanding of the way of working of the data science teams(e)Experience with Data Science will be valuable in this regard.
Who you are
- You have at least 5 years of experience in data science or software engineering.
- You have at least 2 years of experience and a proven track record building / setting up / maintaining tools for Machine Learning in a tech company.
- You have already worked with Python, Git, deep learning frameworks, machine learning tools (MLFlow, Kubeflow, TensorFlow Serving, etc.), cloud services, databases.
- Your working style can be described as collaborative, autonomous, proactive, and structured.
- You are considered a reference by your peers in your domain of expertise.
- You have excellent communication skills.
- You write and speak both French and English.
What we value
We value a positive mindset, collaboration, sense of ownership, transparency, the ability to embrace ambiguity, a talk-less-do-more attitude, and a sense of humor to enjoy ourselves along the way(e)If you identify with our values and want to contribute to our company, we’d love to have you in!
What we offer
- Beautifully by-the-book startup offices in central Paris (we are in the 2nd arrondissement)
- A respectful, fun, collaborative work environment
- Weekly yoga classes
- Mobility packages for public transportation and cycling benefits
- Flexible WFO/WFH policy
- Lunch vouchers (Swile card)
- Daily fresh fruits and healthy (or not) snacks
- Monthly apéros
- Yearly team off-sites
Our Recruiting Process
Being authorised to work in the EU is a precondition of employment.
- Quick introductory phone call
- Quick technical phone call with a 10' tech test in Python
- Technical interview in our office or by Zoom
- Performance interview in our office or by Zoom
- Culture interview (with lunch or drinks with the team if possible!)
- Quick call with our CEO, Yann (only if you did not have the opportunity to meet him in one of the previous interviews)
- Reference calls