Senior Staff Software Engineer

Doctolib

WHAT YOU’LL DO We are looking for a Senior Staff Machine Learning Engineer to join the Clinical team in the AI and Machine Learning Department. You'll lead the technical direction of AI systems that enhance clinical decision-making and patient care. You'll tackle the hardest problems in healthcare AI, from building voice-powered consultation assistants to establishing LLM evaluation frameworks that ensure clinical safety and efficacy. This role combines deep R&D with strategic technical leadership, shaping how Doctolib deploys AI in regulated healthcare environments. Your responsibilities include but are not limited to: Technical Strategy & Architecture * Own the long-term technical roadmap for clinical ML systems, from model architecture selection to production deployment patterns * Drive strategic build vs. buy decisions, balancing custom development with foundation model APIs * Define standards for safe rollout in healthcare contexts: shadow testing, staged deployment, and human-in-the-loop workflows End-to-End ML Delivery * Lead design and implementation of LLM-powered clinical agents (consultation summarization, clinical coding, evidence-grounded recommendations) * Establish rigorous evaluation frameworks using real-world clinical datasets, ensuring outputs meet clinical accuracy and safety standards * Build production infrastructure: model and prompt versioning, guardrails, uncertainty quantification, cost optimization, and observability * Define a bold strategy for online and offline performance objectives to reach new levels of healthcare professionals satisfaction Leadership & Impact * Mentor Staff or Senior ML Engineers and Applied Scientists, elevating technical standards across the organization * Lead cross-functional initiatives spanning Product, Medical Affairs, Legal, and Compliance teams * Translate clinical needs into research questions and production systems that measurably improve patient outcomes * Partner with our medical, legal, and compliance teams to meet EU regulatory expectations (e.g., GDPR, MDR, upcoming EU AI Act) and market standards.   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 have 10+ years in ML/AI with 3+ years at Staff+ or Principal level leading complex, multi-team technical initiatives * You have a PhD in Computer Science, AI, Statistics, or related field (or equivalent research experience) * You have deep expertise in at least two of: clinical NLP, LLM fine-tuning and evaluation, automatic speech recognition, RAG systems, or reinforcement learning * You are expert in Python, PyTorch/Transformers for training, vLLM/similar for inference with a track record deploying ML systems in production (AWS/GCP) * You have exceptional communication skills and are able to align diverse stakeholders and explain complex technical decisions Now it would be fantastic if you have: * Publications in top-tier ML/AI conferences (NeurIPS, ICLR, ACL) or medical informatics venues * Experience with EU healthcare regulations (GDPR, MDR, AI Act) * Prior work with clinical data or healthcare applications 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 Doctolib * 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 Doctolib * 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 * Access to the best AI tools for coding, development and dedicated training THE INTERVIEW PROCESS * HR Screen * Hiring Manager Interview * Behavioral Interview * Technical Questions  * Technical Case Study * At least one reference check   JOB DETAILS * Permanent position * Full-time * Hybrid work mode (3 days/week in the office) * Paris, France * Start date: as soon as possible   If you would like to find out more about tech life at Doctolib, feel free to read our latest Medium blog articles! At Doctolib, 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 Doctolib, regardless of your gender, religion, age, sexual orientation, ethnicity, 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. Join us in building the healthcare we all dream of! All information provided is processed by Doctolib for application management. For data processing details, click here. Please contact hr.dataprivacy(at)doctolib.com for inquiries or to exercise your rights.   * *

Doctolib

WHAT YOU WILL DO At Doctolib, we're revolutionizing healthcare delivery through advanced AI systems focused on medical reasoning. As a Senior Staff Research Scientist, you'll develop cutting-edge AI solutions that enhance clinical decision-making and support healthcare professionals in their daily practice. You'll work at the intersection of machine learning and healthcare, creating models that can understand medical knowledge, reason through complex clinical scenarios, and assist medical practitioners in providing better care. Your responsibilities include (but are not limited to): * Solve Real-World Healthcare Challenges: Design and build advanced machine learning models, especially in NLP and large language models, to improve healthcare workflows, enhance patient experiences, and power intelligent health solutions at scale. * Innovate & Experiment: Research and prototype novel algorithms and architectures, staying at the forefront of AI/ML developments. Bring new ideas from concept to working prototypes, leveraging the latest advances in deep learning and generative AI. * Collaborate Across Teams: Work closely with product, engineering, medical experts, and other stakeholders to translate business needs into impactful research projects. Communicate technical concepts and results clearly to both technical and non-technical audiences. * Drive Impact: Analyze large, complex data sets to extract actionable insights, define key metrics, and rigorously evaluate model performance. * Share & Learn: Advance Doctolib’s AI expertise by sharing findings within the team and with the wider ML community through publications, talks, and workshops. Mentor and support other scientists and engineers in adopting best practices. WHO YOU ARE * Technical Expertise: * PhD or Master’s degree (plus significant experience) in Computer Science, Machine Learning, Mathematics, or a related field; Publications as first author in top-tier AI/ML conferences such as NeurIPS, ICLR, ICML, ACL, EMNLP, or relevant medical informatics venues are a plus; * Deep understanding of machine learning and deep learning concepts, with hands-on experience in NLP, LLMs, or generative AI. * Strong programming skills in Python (and ideally experience with ML frameworks such as PyTorch or JAX). * Familiarity with modern data tools and scalable training on large datasets. * Innovator & Problem Solver: * Track record of independently driving research and translating it into real-world applications. * Experience designing experiments, evaluating intrinsic and extrinsic metrics, and iterating quickly. * Curiosity and drive to learn, research, and apply new machine learning techniques. * Collaborative & Communicative: * Comfortable working in highly cross-functional teams, with the ability to clearly communicate complex concepts to diverse audiences. * Passionate about sharing knowledge and contributing to a culture of learning and innovation. * Mission-Driven: * Motivated by Doctolib’s mission to make healthcare better for all. Eager to solve meaningful problems with technology. * Detail-oriented, rigorous, and committed to delivering high-quality, production-ready solutions. What we offer  * Free comprehensive health insurance 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 Doctolib * 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 * Parent Care Program: receive one additional month of leave on top of the legal parental leave * 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  The interview process   * HR Screen  * Call with Research Team Member 30 mins * Scientific Presentation + Case Study 1 hour 30mins * Behavioral Interview 1 hour 15mins * At least one Reference check   Job details  * Permanent position  * Full Time * Location: Doctolib Paris office in Levallois Perret * Work mode: Hybrid (3 days/week in the office) * Start date: ASAP   If you would like to find out more about tech life at Doctolib, feel free to read our latest Medium blog articles! At Doctolib, 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 Doctolib, regardless of your gender, religion, age, sexual orientation, ethnicity, 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. Join us in building the healthcare we all dream of! All information provided is processed by Doctolib for application management. For data processing details, click here. Please contact hr.dataprivacy(at)doctolib.com for inquiries or to exercise your rights.

Vestiaire Collective

Vestiaire Collective is the leading global platform for desirable pre-loved fashion and a pioneer in transforming how people consume fashion.   Our mission is simple: make circular fashion the norm, not the exception. Through technology, expertise, and a highly engaged global community, we enable millions of people to buy and sell fashion in a more sustainable way.   Founded in Paris in 2009, Vestiaire Collective is now a globally scaled marketplace with offices in Paris, London, Berlin, New York, Singapore, and Ho Chi Minh City, and logistics hubs across Europe, Asia, and the US. Today, we are a team of around 600 people from over 50 nationalities, united by a shared ambition: to drive meaningful change in the fashion industry.   Our values, Activism, Transparency, Dedication, Greatness, and Collective, shape how we build, collaborate, and grow every day. About the Role We are seeking a Foundational Machine Learning Engineer for a high-impact greenfield opportunity to build our MLOps infrastructure from the ground up at Vestiaire Collective. While driving our AI authentication initiatives (deploying multi-model approaches including computer vision for luxury product authentication and counterfeit detection) will be your immediate focus, your long-term mission will be to scale foundational architecture across the entire marketplace. You will expand our ML capabilities to power broader domains, primarily focusing on search and recommendation systems, with future expansions into dynamic pricing and marketing technologies. Acting as the bridge among Applied Science, Data Platform, and Backend Engineering, you will design robust, decoupled architectures and spearhead the MLOps strategy with our Director of Data, prioritizing system maintainability, engineering hygiene, and the reliable deployment of complex models, ensuring all our ML models across the board deliver high-throughput, low-latency business impact. What You Will Do Short-Term Impact (First 6 Months): Partner closely with the Operations squads and Data Scientists to accelerate ML and RAG prototypes into resilient, production-ready code. You will directly integrate with the team to deploy, optimize, and scale heavy-width CV and VLM models focused on fraud detection and luxury product authentication, immediately improving our trust and safety ecosystem. Mid-Term Foundation (MLOps Lifecycle & Infrastructure): Lead the end-to-end foundational groundwork of our ML lifecycle by designing robust systems for Data & Feature Management, Model Tracking & Registry, and Model Serving & Monitoring. You will scale infrastructure by automating continuous retraining pipelines that handle diverse deployment cadences (from daily fraud detection to weekly recommendations), design resilient multi-model architectures, and critically evaluate the technical overhead and TCO of our in-house tools against enterprise-grade platforms to ensure long-term resilience. Long-Term Vision (Centralizing 360-Degree MLE Capabilities): Act as a pioneer and cornerstone hire for the ML engineering discipline at Vestiaire Collective, setting the technical standards to help scale the AI/ML organization. You will transition into a centralized foundational role, moving beyond single-squad operations to mentor the team and provide horizontal ML infrastructure support to multiple domains, including Search, Discovery, Pricing, Marketing, and Data Platforms. Who You Are Must-Haves: Experience: 5-8+ years of hands-on experience in Machine Learning Engineering, specifically focused on building and scaling MLOps infrastructure and productionizing ML systems. Production Infrastructure: Proven expertise in deploying low-latency, high-throughput ML inference services (using FastAPI, TorchServe, Triton Inference Server, or Ray Serve) across both classical lightweight and heavy-width ML models (PyTorch/TensorFlow). Strong preference for AWS (EKS, EC2, SageMaker) / Snowflake and Open Source ecosystems over GCP/Azure. MLOps & Pipelines: Deep experience building automated, continuous model retraining pipelines to handle concept drift (ranging from daily to weekly cycles). You have orchestrated decoupled, multi-model AI architectures using tools like Airflow, Kubeflow, or Metaflow, and possess strong expertise in model registry and tracking tools like MLflow or Weights & Biases. Feature Stores: Hands-on experience evaluating, building, or extensively leveraging online (Redis, DynamoDB) and offline (Snowflake, S3) Feature Stores in a production environment. Familiarity with frameworks like Feast or custom dbt-based pipelines is highly valued. Strategic Builder Mindset: You are an analytical builder who thinks long-term. You can successfully evaluate TCO for bespoke internal systems versus enterprise tools, anticipate technical liabilities, and design robust architectures that handle unpredictable peak traffic surges. Collaboration & Engineering Hygiene: Strong cross-functional communication skills. You excel at translating complex ML prototypes into highly scalable production code backed by strict version control, rigorous testing, and CI/CD best practices, seamlessly connecting data science innovation with backend engineering execution. Nice-to-Haves: Relevant Domain Expertise: Background in E-commerce, Single-SKU Marketplaces, Search & Recommendation, Trust & Safety, or Counterfeit Detection. Vision, Edge & Optimization: Hands-on experience with Vector Databases, Visual RAG pipelines, deploying Deep Learning VLM models, and optimizing models for edge computing or low-latency inference (e.g., ONNX, TensorRT). Infrastructure & Observability: Advanced experience with containerization (Docker, Kubernetes), Infrastructure as Code (Terraform), and data transformation workflows (dbt). Familiarity with setting up advanced monitoring for model performance, concept drift, and system health (Datadog, Prometheus).