Machine Learning Engineer

We are on a mission to make people healthier.

Vita 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.

Our 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 the B2C sector, where we began, but also in B2B by integrating into corporate welfare programs for both large enterprises and SMEs that shape our market. Hence, we are looking for a Machine Learning Engineer to join our tech team and drive the next phase of our business evolution.

About the role

We'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.

This 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.

What you'll do

Design, develop, and iterate on machine learning models and generative AI features across staging, and production environments

Build and maintain LLM-powered pipelines, including prompt engineering and agentic workflows

Fine-tune, evaluate, and benchmark foundation models for domain-specific tasks, using both open-source and API-based LLMs

Package and deploy models as production services, working with containerisation tools and cloud infrastructure (e.g. AWS SageMaker, Lambda, ECS, or equivalent)

Implement model monitoring, evaluation pipelines, and alerting to track performance degradation, data drift, and output quality over time

Contribute to MLOps practices: versioning datasets and models, reproducible training pipelines, and experiment tracking

Integrate observability tooling (logging, tracing, dashboards) into ML services to ensure production readiness and incident response readiness

Collaborate with backend engineers to expose model capabilities through well-defined APIs and event-driven interfaces

Write clean, testable Python code and contribute actively to sprint delivery and cross-team reviews

Document experiments, model decisions, evaluation results, and runbooks for production systems

What we're looking for

Solid hands-on experience in ML engineering, with a track record of shipping models to production (not just notebooks)

Strong Python skills, nice to have knowledge of libraries such as PyTorch, Hugging Face Transformers, LangChain/LlamaIndex, and FastAPI or equivalent serving frameworks

Practical experience working with LLMs; prompt design, RAG architectures, evaluation strategies, and integrating LLM APIs (OpenAI, Anthropic, Mistral, etc.)

Experience deploying and serving ML models in cloud environments (AWS preferred), including containerisation with Docker and orchestration basics

Understanding of MLOps principles: experiment tracking, model registry, CI/CD for ML pipelines, and dataset versioning

Demonstrated experience instrumenting ML systems for production observability: latency tracking, output quality metrics, drift detection, and structured logging

Comfort working with data pipelines and storage systems (e.g. S3, PostgreSQL, DynamoDB etc…)

Strong evaluation mindset: ability to design robust offline and online evaluation frameworks for generative AI outputs, including human-in-the-loop and automated approaches

Collaborative, pragmatic approach to engineering — able to balance research curiosity with delivery focus and production discipline

Good communication skills, comfortable presenting findings, trade-offs, and model behaviour to both technical and non-technical stakeholders

Nice to have

Experience with guardrails and responsible AI practices for LLM outputs (toxicity filtering, hallucination detection, PII redaction)

Familiarity with vector search and semantic retrieval at scale

Exposure to multi-modal models or agents

Experience with vector databases such as Pinecone

What We Offer

💰 Competitive compensation: €40.000-€45.000 + bonus 5%-10% (depending on seniority)

🛍️ €2,000 Welfare yearly budget: fully flexible and usable across a wide range of services (groceries, gasoline, travels, mental health services)

🧘♀️ True flexibility: Work from anywhere, or join us in our beautiful Milan office — your call.

🎂 Birthday off — because no one should work on their birthday.

🌸 Menstrual leave: Up to 12 additional days off per year, because wellbeing is more than just a buzzword.

🏩 Free access to the Vita Health app — which includes nutritionists, trainers, and doctors, extended to your family. Because if we don’t live our product, how can we live our mission?

⚖️ Your "Lawyer in your pocket": Direct, free access to professional legal support for you and your family. From checking house contracts to resolving online purchase disputes, Lexy provides expert guidance within 24 hours.

📚 Continuous growth: A personalised Individual Development Plan (IDP) from day one, evolving with your goals and aspirations — plus a dedicated learning budget and weekly learning time.

🔁 Structured career progression: We’re serious about your growth — with 4 formal reviews per year, ongoing 1:1s, and two salary adjustment opportunities annually (not mutually exclusive), meaning outstanding performance can be rewarded every six months.

💡 Real impact: Your ideas won’t sit in a backlog — they’ll shape the future of health.

🤝 Supportive leadership: We believe in enabling, not micromanaging.

❤️ Join a team where purpose meets action — a unique opportunity to have a significant impact in the health sector

Recruiting Process

We 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:

👋 Intro Call with HR

📋 Technical test

🛠️ Technical Interview with the Head of Engineering

🤝 Cultural & Leadership Interview

🧭 Final Interview with our CEO