Sr Engineer, Software Backend
- 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...
- 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.
- Type of contract: CDI
- Location: Paris, France
- This is not an active vacancy but part of our ongoing talent planning for future opportunities.
- 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.)
- Fit interview (30min)
- Technical Interview 1 (1h)
- Technical Interview 2 (1h30)
- C-level interview (30 min)
- Databases: SQL Postgres with sqlalchemy
- ML: Python, Torch, ClearML, Ray
- Infrastructure: GCP, Kubernetes, CircleCI
- Backend: Python, Flask, Rabbitmq, OpenAPI
- Frontend: JS, TS, React
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