About Monk:
Monk provides visual expertise through Computer Vision and Deep Learning algorithms. Today, our AI model is able to detect and classify damages on any vehicle, with photos taken from a smartphone. The company is already working with international leading players, such as our parent company ACV Auctions (USA), but also CAT logistics (Europe) Getaround (France), Autobiz (Europe) or Hgreg (Canada). We are building a world-class API-based damage detection system.
We are looking for a great engineer with a data-centric approach to help us build a high-performance Data Engineering infrastructure to support the efforts of our ML team. Come and join us to build the future of vehicle damage detection. .
Missions:
Design and build scalable applications for Monk ML Engineers to facilitate common tasks:
Run and evaluate models
Train models
Retrieve and store data
Annotate and generate data
Review and improve data
Champion a data-centric approach to leverage all the unstructured and structured data of Monk: active learning, confident learning, dataset iteration… Help to avoid some common ML issues (under specification, fruitless model fiddling…) and work on the big lever that is high-quality data.
Define ML ops best practices to help the data infrastructure of Monk.ai scale. Engage with ML Engineers to identify bottlenecks and pain points in the ability of Monk R&D to constantly offer new and better ai services.
Integrate and arbitrate between multiple stakeholders and constraints including: ai performance, quality of life for ml scientists, cost-efficiency of labeling and infrastructure etc…
Skills:
Extensive backend engineering and software engineering experience, building backend applications that scales.
Being very comfortable with SQL and NoSQL databases, which to choose when building a system and how to use them effectively.
Engineering skills related to data-intensive applications, for scalability, reliability, robustness and observability.
A mindset for data-oriented decision-making: being obsessed about measuring result data and acting on it.
An understanding of Deep Learning Models and the constraints related to build, use and evaluate them at scale.
Contract:
Type of contract : CDI
Location : Paris, France
Benefits:
At Monk, we’re committed to building an equitable and inclusive global economy. And we can’t do this without our most important asset—You. That’s why we offer benefits to help you thrive in every stage of life. We champion your financial, physical, and mental health by offering valuable benefits and resources to help you care for the whole you.
We have great benefits including:
Hybrid work environment: possibility of being in full remote or working in the office
Restaurant tickets with Swile and sport via Gymlib
Fair and competitive compensation
Health Insurance with Alan
The premises are at Agoranov and Platform 58: the ecosystem is top notch and super dynamic.
The team is ambitious (Profiles: X, CentraleSupélec, UTC) and supported by references from AI (professor at l’X in AI, PhD ENS etc.)
Our Company Stack:
Databases: SQL Postgres with sqlalchemy
ML: Python, Torch, ClearML, Ray
Infrastructure: GCP, Kubernetes, CircleCI
Backend: Python, Flask, Rabbitmq, OpenAPI
Frontend: JS, TS, React
Interview process:
Fit interview (30min)
Technical Interview 1 (1h)
Technical Interview 2 (1h30)
C-level interview (30 min)
We want you to bring your whole human self to work every day. We accept you for who you are and consider everybody on an equal opportunity basis without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status Monk collects your personal data for the purposes of managing Monk’s recruitment related activities. Consequently.
Monk may use your personal data in relation to the evaluation and selection of applicants. Your information will be accessible to Monk and its affiliated entities across the world. By submitting your application, you expressly give your consent to this use and processing of your personal data.
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