Paris, Paris, France
Live, first seen by us 34 days agoJob details Permanent position Tech stack: Python / Terraform / Kubernetes
Tech stack: Python / Terraform / Kubernetes Full-time Paris, France
Employment type in the employer’s posting: Unlimited ContractFrom the employer’s posting
Take models from experimentation to reliable, scalable production with data scientists and ML engineers
Operate the orchestration layer for training, serving and evaluation on GKE and Ray
Build Terraform modules, operators and GitOps tools so ML teams can deploy models safely
Define and track SLOs, and take part in on-call and incident response
Build observability for model health and quality using Datadog and Braintrust
Partner with data scientists, ML & AI engineers to take models from experimentation to reliable, scalable production deployment.
Operate and evolve the orchestration layer for training, serving and evaluation workloads, built on GCP and AWS using Kubernetes (GKE) and Ray-based platforms (Anyscale)
Build the tools, templates and best practices (Terraform modules, Kubernetes CRDs and operators, GitOps workflows, internal libraries)
define and track SLOs; take part in on-call and incident response for the whole stack
Build and maintain observability for ML systems: infrastructure and model health via Datadog, and evaluation/quality signals via Braintrust
From the employer’s posting.
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Day 34
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From 4,444 similar postings that came down, and 14 of Doctolib’s. Coming down is not always a hire.
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- Our solutions are built on a single fully cloud-native platform that supports web and mobile app interfaces, multiple languages, and is adapted to country and healthcare specialty requirements. - Our stack is composed of Rails, TypeScript, Java, Python, Kotlin, Swift, and React Native. - We leverage AI ethically across our products to empower patients and health professionals. Discover our AI vision here.
- Have prior experience as an MLOps Engineer, Cloud Engineer for ML applications, AI Infrastructure Engineer, or Platform Engineer for AI, or in a similar role - Are proficient in Python, Shell scripting and Terraform, with a solid understanding of machine learning algorithms, concepts and trends - Have hands-on experience with Kubernetes in production and GitOps tools (e.g. ArgoCD), and have built MLOps pipelines for containerizing models and solutions with Docker
- Permanent position - Tech stack: Python / Terraform / Kubernetes - Full-time
- Partner with data scientists, ML & AI engineers to take models from experimentation to reliable, scalable production deployment. - Operate and evolve the orchestration layer for training, serving and evaluation workloads, built on GCP and AWS using Kubernetes (GKE) and Ray-based platforms (Anyscale) — including CI/CD, versioning and automated testing pipelines that carry a model from the registry to a served endpoint. - Build the tools, templates and best practices (Terraform modules, Kubernetes CRDs and operators, GitOps workflows, internal libraries) that let any ML team deploy and operate their models and agents safely and consistently.
- Have hands-on experience with Kubernetes in production and GitOps tools (e.g. ArgoCD), and have built MLOps pipelines for containerizing models and solutions with Docker - Have experience with cloud platforms such as GCP (GKE, BigQuery) and/or AWS or Azure equivalents, and with observability tools such as Datadog - Have hands-on experience with an experiment tracking/model registry tool (e.g. MLflow) and an LLM evaluation/observability tool (e.g. Braintrust)
- Ensure the availability, reliability and performance of ML systems in production across training, serving and the agents platform; define and track SLOs; take part in on-call and incident response for the whole stack. - Build and maintain observability for ML systems: infrastructure and model health via Datadog, and evaluation/quality signals via Braintrust, catching regressions before they reach users. - Work hand in hand with AI inference engineers on the shared foundations of our self-hosted LLM stack (model registry, deployment automation, shared GPU capacity), and stay current with MLOps practices for classic ML, generative AI and agentic systems alike.
- Are proficient in Python, Shell scripting and Terraform, with a solid understanding of machine learning algorithms, concepts and trends - Have hands-on experience with Kubernetes in production and GitOps tools (e.g. ArgoCD), and have built MLOps pipelines for containerizing models and solutions with Docker - Have experience with cloud platforms such as GCP (GKE, BigQuery) and/or AWS or Azure equivalents, and with observability tools such as Datadog
- Operate and evolve the orchestration layer for training, serving and evaluation workloads, built on GCP and AWS using Kubernetes (GKE) and Ray-based platforms (Anyscale) — including CI/CD, versioning and automated testing pipelines that carry a model from the registry to a served endpoint. - Build the tools, templates and best practices (Terraform modules, Kubernetes CRDs and operators, GitOps workflows, internal libraries) that let any ML team deploy and operate their models and agents safely and consistently. - Ensure the availability, reliability and performance of ML systems in production across training, serving and the agents platform; define and track SLOs; take part in on-call and incident response for the whole stack.
It would be fantastic if you: - Have hands-on experience with Kubernetes-native agent abstractions (CRDs/operators, e.g. kagent) or building production agent runtimes - Have experience with ML model quantization and optimization
- Have experience with cloud platforms such as GCP (GKE, BigQuery) and/or AWS or Azure equivalents, and with observability tools such as Datadog - Have hands-on experience with an experiment tracking/model registry tool (e.g. MLflow) and an LLM evaluation/observability tool (e.g. Braintrust) - Are a strong team player with excellent communication and documentation skills, comfortable with an evolving scope, and pragmatic about build vs. buy decisions
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New ML Engineer postings at the employers we follow are up 12% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
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Paris, Paris, France
- Enrollment in Doctolib's long-term employee value sharing plan called DoctoGrowth - For caregivers and workers with disabilities, a package including an adaptation of the remote policy, extra days off for medical reasons, and psychological support - Relocation support in case of international mobility
- Paris, France - Hybrid work setup (up to 2 remote days per week) - Start date: as soon as possible
No clear work-authorization passage found. Eligibility is unconfirmed.