Senior ML/AI Engineer, Hublo
€75-100k
OTE
3-5 days a week in office
HR software for healthcare
Be an early applicant
HR software for healthcare
201-500 employees
Be an early applicant
€75-100k
OTE
3-5 days a week in office
201-500 employees
Company mission
Make everyday life much easier for health care professionals.
Role
Who you are
- 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)
- 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
What the job involves
- 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
- 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
- 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
- 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
- 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
- Model and service latency and availability within agreed SLAs
- Drift and incident rate kept below threshold; time-to-recovery after issues
- Time from prototype to production; cadence of meaningful iterations per quarter
- Experiment throughput with clear learnings (A/B tests, offline evaluations → shipped features)
Application process
- Un Phone Screen(30mn)
- Une manager Itw(1H)
- Un skill test (1h)
- Un cultural add(1h)
Our take
In the realm of European healthcare, where a staggering 15 million professionals are dedicated to saving lives, the burden of administrative tasks often takes precious time away from their critical work. Enter Hublo, a software company streamlining administrative processes and cutting operational costs for healthcare professionals.
Hublo's digital platform is designed with user-friendliness in mind, catering to the needs of hospitals, clinics, and retirement home organisations. In an industry where traditional pen-and-paper methods still dominate, staffing and resource management remain a significant challenge. Hublo seeks to revolutionise the healthcare landscape by offering a solution that simplifies these complex processes.
With an already established presence in France and Germany, Hublo is poised for ambitious geographic expansion, targeting the opening of four additional countries. While the start-up has made significant strides in solving healthcare HR challenges, the next frontier lies in the development of a vertical software suite that not only addresses the complexities of this overlooked category but also generates substantial revenue. As Hublo continues to evolve, it has the potential to make a profound impact on the healthcare industry, enabling professionals to focus more on their vital work of saving lives.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
42% employee growth in 12 months
Company
Funding (last 2 of 5 rounds)
Feb 2026
$46.3m
LATE VC
Oct 2025
$23.2m
LATE VC
Total funding: $96.2m
Company benefits
- Professional growth: a dynamic, human-scale structure that values initiative and dedication
- Dynamic locations: our vibrant office on Rue de Paradis provides an inspiring setting. You'll also have a unique opportunity to work in our Cologne office in Germany
- 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
- Alan healthcare insurance: online insurance, 50% 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
Company HQ
Roquette, Paris, France
Leadership
Antoine Loron
(Co-Founder & CEO)Previously a consultant, Antoine worked at A.T. Kearney before joining Roland Berger and was promoted multiple times to become a Senior Consultant.
Adrien Beata
(Co-Founder)Worked as a data analyst for fifty-five before joining AXA as a Digital Performance Manager.
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