ppWe are on a mission to make people healthier. /p pVita Health is a dynamic startup in the health, nutrition, and fitness industry. Our innovative product has positively affected the lives of over 130,000 people in our core market, and we are now eager to expand our expertise and reach globally. /p pOur mission is to make prevention accessible and affordable, empowering individuals to take control of their health and achieve optimal well-being. Now we want to enhance our presence not just in bB2C /b sector, where we began, but also in bB2B /b by integrating into corporate bwelfare programs /b for both large enterprises and SMEs that shape our market. Hence, we are looking for a bMachine Learning Engineer /b to join our tech team and drive the next phase of our business evolution. /p h3About the role /h3 pWe're looking for a Machine Learning Engineer to research, build, and deploy intelligent systems that put machine learning and generative AI into the hands of our users and products. /p pThis is a hands-on role sitting at the intersection of data science and engineering. You'll work across the full model lifecycle, from experimentation and fine-tuning to production deployment with a strong emphasis on LLM-powered features, Agentic AI solutions, and model observability. You'll collaborate closely with Backend and Product teams to ship reliable AI systems that are measurable, maintainable, and safe in production. /p h3What you'll do /h3 ul liDesign, develop, and iterate on machine learning models and generative AI features across staging, and production environments /li liBuild and maintain LLM-powered pipelines, including prompt engineering and agentic workflows /li liFine-tune, evaluate, and benchmark foundation models for domain-specific tasks, using both open-source and API-based LLMs /li liPackage and deploy models as production services, working with containerisation tools and cloud infrastructure (e.g. AWS SageMaker, Lambda, ECS, or equivalent) /li liImplement model monitoring, evaluation pipelines, and alerting to track performance degradation, data drift, and output quality over time /li liContribute to MLOps practices: versioning datasets and models, reproducible training pipelines, and experiment tracking /li liIntegrate observability tooling (logging, tracing, dashboards)



into ML services to ensure production readiness and incident response readiness /li liCollaborate with backend engineers to expose model capabilities through well-defined APIs and event-driven interfaces /li liWrite clean, testable Python code and contribute actively to sprint delivery and cross-team reviews /li liDocument experiments, model decisions, evaluation results, and runbooks for production systems /li /ul h3What we're looking for /h3 ul liSolid hands‑on experience in ML engineering, with a track record of shipping models to production (not just notebooks) /li liStrong Python skills, nice to have knowledge of libraries such as PyTorch, Hugging Face Transformers, LangChain/LlamaIndex, and FastAPI or equivalent serving frameworks /li liPractical experience working with LLMs; prompt design, RAG architectures, evaluation strategies, and integrating LLM APIs (OpenAI, Anthropic, Mistral, etc.) /li liExperience deploying and serving ML models in cloud environments (AWS preferred), including containerisation with Docker and orchestration basics /li liUnderstanding of MLOps principles: experiment tracking, model registry, CI/CD for ML pipelines, and dataset versioning /li liDemonstrated experience instrumenting ML systems for production observability: latency tracking, output quality metrics, drift detection, and structured logging /li liComfort working with data pipelines and storage systems (e.g. S3, PostgreSQL, DynamoDB etc…) /li liStrong evaluation mindset: ability to design robust offline and online evaluation frameworks for generative AI outputs, including human‑in‑the‑loop and automated approaches /li liCollaborative, pragmatic approach to engineering — able to balance research curiosity with delivery focus and production discipline /li liGood communication skills, comfortable presenting findings, trade‑offs, and model behaviour to both technical and non‑technical stakeholders /li /ul h3Nice to have:



/h3 ul liExperience with guardrails and responsible AI practices for LLM outputs (toxicity filtering, hallucination detection, PII redaction) /li liFamiliarity with vector search and semantic retrieval at scale /li liExposure to multi‑modal models or agents /li liExperience with vector databases such as Pinecone /li /ul h3What We Offer /h3 ul lib€2,000 Welfare yearly budget: /b fully flexible and usable across a wide range of services (groceries, gasoline, travels, mental health services) /li libTrue flexibility /b: Work from anywhere, or join us in our beautiful Milan office — your call. /li libBirthday off /b — because no one should work on their birthday. /li libMenstrual leave /b: Up to b12 additional days off per year /b, because wellbeing is more than just a buzzword. /li libFree access to the Vita Health app /b — which includes nutritionists, trainers, and doctors, extended to your family. Because if we don’t live our product, how can we live our mission? /li libYour "Lawyer in your pocket": /b Direct, free access to professional legal support for you and your family. From checking house contracts to resolving online purchase disputes, bLexy /b provides expert guidance within 24 hours. /li libContinuous growth /b: A personalised bIndividual Development Plan (IDP) /b from day one, evolving with your goals and aspirations — plus a dedicated learning budget and weekly learning time. /li libStructured career progression /b: We’re serious about your growth — with b4 formal reviews per year /b, ongoing 1:1s, and btwo salary adjustment opportunities annually /b (not mutually exclusive), meaning outstanding performance can be rewarded every six months. /li libReal impact /b: Your ideas won’t sit in a backlog — they’ll shape the future of health. /li libSupportive leadership /b: We believe in enabling, not micromanaging. /li liJoin a team where purpose meets action — a unique opportunity to have a significant bimpact /b in the health sector /li /ul pWe believe hiring is a two‑way conversation: while we assess your fit for the role, you'll also get the chance to evaluate if we're the right place for your next career move. Here's what you can expect: /p ul liIntro Call with HR /li liTechnical test /li liTechnical Interview with the Head of Engineering /li /ul /p #J-18808-Ljbffr

📌 Machine Learning Engineer- (100% Remote) (Italia)
🏢 Vita Health
📍 Italia