Senior ML/AI Engineer(x/f/m) at Hublo

Paris, Ile-de-France, France -

Full Time

Start Date

Immediate

Expiry Date

10 Oct, 26

Salary

100000.0

Posted On

12 Jul, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, TensorFlow, PyTorch, Scikit-learn, SQL, Generative AI, LLM APIs, LangChain, LlamaIndex, MLOps, CI/CD, AWS, GCP, Azure, SageMaker, Vertex AI

Industry

Software Development

Description

As the first Senior ML/AI Engineer at Hublo, you will help us build our AI and Machine Learning capabilities from the ground up. We are at the beginning of this journey, and your primary mandate is to identify which problems are truly worth solving with AI and ML. As part of the Data Platform team, you will explore, prototype, and ship intelligent features powered by ML and LLMs that deliver measurable value to healthcare professionals. You'll work closely with Product to identify high-impact AI opportunities, then partner with both Engineering and Data teams to build, deploy, and maintain production solutions. This role directly addresses a core challenge: reducing administrative friction for caregivers and health managers so they can spend more time on patient care but only where AI proves to be the right solution. Delivery & Experimentation : Ship AI Features That Work Own end-to-end delivery of AI/ML features, from problem framing to production deployment and iteration. Cover the full ML/AI scope: classical ML (recommendations, predictions, optimization) and LLM-based features (assistants, document understanding, search). Run disciplined POCs with clear success metrics, baseline comparisons, and go/no-go criteria defined upfront with Product. Make pragmatic technical decisions on modeling approaches, data requirements, evaluation methods, and build vs buy trade-offs. Kill what doesn't work: document learnings from failed experiments and redirect resources quickly. Ensure production quality: latency, reliability, observability, security, and graceful degradation when models fail. Foundations : Build Reusable AI Infrastructure Set initial standards for ML/LLM engineering: experiment tracking, prompt/model versioning, evaluation harnesses, and documentation templates. Build reusable components where it unblocks velocity: shared datasets, template pipelines, monitoring dashboards, keep it pragmatic. Lay the groundwork for MLOps/LLMOps: CI/CD for models, A/B testing infrastructure, basic drift/quality monitoring. Document "how we do AI at Hublo": evaluation rules, production checklists, and safety guidelines. Collaboration & Enablement : Partner and Share Knowledge Work closely with Product to identify high-impact AI use cases, shape scope based on feasibility, and align on success metrics. Partner with Engineering on integration, performance requirements, and operational reliability, own the AI/ML part, collaborate on the rest. Communicate clearly on uncertainty: especially for LLM limitations, expected quality, and trade-offs, set realistic expectations early. Lead and animate the AI Community of Practice: drive discussions, share patterns and learnings, ensure it stays active and valuable for the company. Grow AI literacy with short, practical sessions: what works when, how to evaluate AI outputs, common pitfalls to avoid. Share openly: write postmortems (including failures), document technical decisions, and make your work reusable. Encourage evidence over hype: measurable outcomes, honest limitations, and realistic timelines, set the tone for how Hublo builds AI. Measure of success Product Impact Uplift on key user outcomes from AI features (e.g., +X% reduced task duration, −Y% time-to-action). Feature adoption and retention for AI-powered workflows (weekly active users, repeat usage). Quality gains: recommendation precision/recall, summarization quality scores, CSAT on AI features. Reliability & Performance Model and service latency and availability within agreed SLAs. Drift and incident rate kept below threshold; time-to-recovery after issues. Delivery & Iteration Speed Time from prototype to production; cadence of meaningful iterations per quarter. Experiment throughput with clear learnings (A/B tests, offline evaluations → shipped features). Skills Hard Skills Advanced Python proficiency; solid experience with ML frameworks (TensorFlow, PyTorch, scikit-learn). Strong SQL skills: complex queries, performance tuning, data modeling basics. Generative AI expertise: LLM APIs (OpenAI, Claude,…), LangChain/LlamaIndex. MLOps experience: CI/CD pipelines, model monitoring, deployment at scale. Cloud platform experience (AWS/GCP/Azure) and managed ML services (SageMaker, Bedrock, Vertex AI). Soft Skills Product mindset with a bias for measurable impact and ROI. Clear communication with non-technical partners; ability to write crisp documentation (ex: diving / specifications / exploration / problem statement / …). Strong ownership and autonomy; pragmatic problem-solving approach. Collaborative spirit with Product, Design, and Engineering teams; embraces feedback culture. Why this role matters ? At Hublo, AI and Machine Learning are not just technical capabilities. They are strategic levers to transform healthcare operations. This role directly contributes to improving the efficiency of the healthcare system by reducing administrative burden for caregivers and health managers, allowing them to focus on what truly matters: patient care. As our Senior ML/AI Engineer, you will have a rare opportunity to shape the future of AI at Hublo. You will co-build the AI roadmap, drive technical acculturation across teams, and collaborate with leadership, Product, Engineering, and the Head of Data to identify and prioritize high-impact initiatives. You will enjoy a high level of autonomy, the freedom to innovate, and direct influence on the product roadmap. Beyond the technical challenge, you will be part of a mission-driven company where your work has tangible societal impact—helping healthcare professionals work better, so they can care better. If you are passionate about leveraging cutting-edge AI to solve real-world problems in a meaningful domain, this is your opportunity to make a difference. The experience we offer 🎯 Impact-first mission: our focus on the healthcare sector offers a purpose-driven career. 💶 Competitive compensation: a total salary package ranging from 75k€ to 100K€ OTE per year based on your experience 👣 Professional growth: a dynamic, human-scale structure that values initiative and dedication. 🌱 Responsible work environment: we are B-Corp certified, acknowledging our commitment to continuously grow and improve as an environmentally and socially responsible company. 🗼 Dynamic locations: our vibrant office on Rue de Paradis provides an inspiring setting. 🏡 Hybrid work policy: flexible work arrangement—up to 10 remote days a month. 🤲 Strong onboarding: a comprehensive program, guiding you through your initial weeks at Hublo. 💪 Team cohesion: build strong connections with colleagues through regular team events and an annual seminar, ensuring a connected and collaborative work environment. We also care about your well-being with tangible perks: ⛑️ Benefiz healthcare insurance: 70% of it paid by Hublo 🥗 A Swile Card: Providing you with access to €11/day in meal vouchers, 50% covered by the company 🍱 🏋️‍♂️ Access to a variety of sports activities through our partner Gymlib🤸🏼🏋🏻 Your recruitment process Un Phone Screen(30mn) Une manager Itw(1H) Un skill test (1h) Un cultural add(1h)

Responsibilities

Build Hublo's AI and Machine Learning capabilities from the ground up by identifying high-impact problems and shipping intelligent features. Establish AI infrastructure, MLOps standards, and lead the AI Community of Practice to reduce administrative friction in healthcare.