Senior IA Inference Engineer (x/f/m)

Doctolib; Paris (TX); Hybrid; USD 122,000 - 167,000; Full time

Benefits offered by this job

Free comprehensive health insurance (h

25 days of paid vacation per year

Lunch vouchers (Swile card)

Relocation support for international移動

DoctoGrowth employee value sharing

Job summary

Doctolib is seeking a Senior Inference Engineer to join the ML Platform (TIGER) team in Paris. You will own the self-hosted LLM serving platform, deploy and scale open-weight models, and drive cost efficiency across our GPU fleet while ensuring security and compliance for patient data.

You will collaborate with Security and Privacy teams, shape deployment automation on Ray Serve / Anyscale, and improve serving performance with advanced inference techniques.

Qualifications

• 5+ years of software or computer engineering experience.
• Hands-on production experience with at least one LLM serving engine.
• Proficient in Python and Terraform; strong understanding of transformer inference mechanics.

Responsibilities

• Build the shared LLM serving foundations with MLOps engineers and define the serving stack.
• Own the model registry and promotion path; benchmark performance and output quality.
• Optimize inference performance and efficiency across GPU fleet with batching, caching, and quantization.
• Own production reliability: define SLOs, run load tests, implement safe rollouts and autoscaling.
• Make serving performance and costs measurable with Datadog dashboards and cost reporting.

Skills

Python programming

Terraform

GPU workload management

Kubernetes

Communication skills

Tools

Ray Serve

Anyscale

vLLM / TensorRT-LLM

Triton Inference Server

SGLang

Job description

Set a new pulse for healthcare!We are looking for a Senior Inference Engineer to join the ML Platform (TIGER) team in AI & Machine Learning.

Your mission will be to power the company's AI-driven healthcare products — from Consultation Assistant to Phone Assistant and Health Companion — by owning the self-hosted LLM serving platform that keeps the most sensitive patient data within infrastructure the company controls. You will build and operate the shared foundations that every AI feature team relies on: deploying, tuning, and scaling open-weight models across our GPU fleet, contributing directly to faster, safer, and more cost-efficient AI for care teams and patients across Europe.

Working in the tech team at the company means building innovative products and features to improve the daily lives of care teams and patients.

What you'll do

• Build the shared LLM serving foundations with MLOps engineers — define the serving stack, GPU capacity planning, and deployment automation for open-weight models (Gemma, Mistral, Llama, Qwen, and their fine-tuned variants) on Ray Serve / Anyscale, while shaping the evolution toward Kubernetes-based orchestration

• Own the model registry and promotion path — establish clear processes for versioning, validating, and promoting models into production, benchmark performance and output quality under realistic workloads, and maintain the curated model catalog together with the Agents Platform team

• Optimize inference performance and efficiency — operate and tune serving engines (vLLM, Triton Inference Server, TensorRT-LLM) to reduce time-to-first-token, inter-token latency, and cost per token through batching, caching, quantization, and parallelism strategies across our GPU fleet (L4 to H200)

• Own production reliability and disaster recovery — define SLOs, run load tests before launches, implement safe rollouts, rollbacks, and autoscaling, and participate in on-call and incident response

• Make serving performance and costs measurable — build Datadog dashboards, alerting, and cost reporting to track latency, GPU utilization, and inference spend, and identify opportunities to improve resource efficiency

• Keep the inference stack secure, compliant, and evolving — work with Security and Privacy on network isolation, data residency, and auditability, while continuously assessing new models, serving engines (such as SGLang), and GPU generations

Who you are

Before you read on: if you don't have the exact profile described below, but you feel this job description matches your skill set, we still encourage you to apply.

You'll be a great fit if you:

• Have 5+ years of software or computer engineering experience, including hands-on production experience with at least one LLM serving engine (vLLM, SGLang, TensorRT-LLM, TGI, or Triton) — ideally having chosen, introduced, tuned, or scaled one in production, not just run it with defaults

• Are proficient in Python and Terraform, with a solid understanding of transformer inference mechanics: prefill vs. decode, KV cache, batching strategies, and why a model is compute- or memory-bound on a given GPU

• Have strong experience running GPU workloads on Kubernetes or a managed Ray / Anyscale platform, and a performance engineering mindset — you design reproducible benchmarks, read profiler output, and back decisions with numbers

• Are comfortable owning production reliability: observability, incident response, and on-call are a natural part of the role

• Have excellent communication and collaboration skills, high agency, a pragmatic build-vs-buy approach, and are comfortable working in a scope that is still taking shape

• Are fluent in English

It would be fantastic if you:

• Have experience with distributed serving frameworks (KServe, llm-d, NVIDIA Dynamo), including disaggregated prefill/decode or KV-cache-aware routing, or with serving Mixture-of-Experts, multimodal models, or many LoRA adapters on shared base models

• Have GPU kernel experience or deep profiling skills (CUDA, Triton, Nsight), or have contributed to open-source inference projects

• Have experience in regulated environments (healthcare, finance, public sector) and with frameworks such as GDPR, HDS, ISO 27001, or SOC 2

Our stack

• Programming languages: Python, plus comfort reading/debugging a systems language (C++, Rust, or Go)

• Cloud providers: GCP / AWS / Azure

• Serving orchestration: Ray Serve / Anyscale (GCP), with Kubernetes (GKE) + GPU scheduling as an emerging parallel target

• LLM serving engines (in production): vLLM, Triton Inference Server, TensorRT-LLM (also evaluating SGLang and other emerging engines)

• Models: Self-hosted open-weight LLMs (Gemma, Mistral, Llama, Qwen), fine-tuned in-house

• Experiment tracking & model registry: MLflow

• GenAI gateway: LiteLLM

• LLM evaluation & observability: Braintrust

• GPUs: NVIDIA L4, A10G, L40S, RTX 6000 Pro, A100 (40GB/80GB), H200

• Container / Infra as code: Docker, Terraform, Helm, Kustomize

• Observability: Datadog

• Data warehouse / storage: GCP BigQuery / GCS / AWS S3

• And any other tool you deem relevant!

Life at the company Tech

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.

Want to learn more about our tech culture and environment?

Visit the the company Tech site.

What we offer

• Free comprehensive health insurance (basic package) for you and your children

• 25 days of paid vacation per year, plus up to 14 days of RTT

• Free mental health and coaching services through our partner Moka.care

• Work from abroad for up to 10 days per year thanks to our flexibility days policy

• Lunch vouchers (Swile card) worth €8.50 per working day, with €4.50 covered by the company

• A subsidy from the work council to refund part of the membership to a sport club or a creative class

• 50% reimbursement of your public transport subscription

• ParentCare Program: Enjoy full salary coverage (100%) during your first month of birth leave and 75% during the second, covered by the company

• Enrollment in the company'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

• Access to the best AI tools for coding, development and dedicated training

Our interview process

• HR interview by phone (45 minutes)

• Hiring manager interview (1 hour)

• Technical case study (1 hour)

• Behavioral interview (1 hour 30 minutes)

• At least one reference check

Job details

• Permanent position

• Tech stack: Python, vLLM, TensorRT-LLM, Triton, Ray Serve

• Full-time

• Paris, France

• Hybrid work setup (up to 2 remote days per week)

• Start date: as soon as possible

We welcome everyone

At the company, we are committed to improving access to healthcare for everyone. This translates into our recruitment process. We evaluate candidates based solely on qualifications and motivation, without any form of discrimination.

The more diverse ideas are heard, the more our product will truly improve healthcare for all. You are welcome to apply to the company, regardless of your gender, religion, age, sexual orientation, ethnicity, or disability.

To ensure equal opportunities, we invite you to exclude personal information (e.g., pictures, age) from your applications. If you require any accommodation, please let us know for support during the hiring process.

Your data privacy

All information provided is processed by the company for application management. For data processing details, click here: France. Please contact hr.dataprivacy(at)the company.com for inquiries or to exercise your rights.