Machine Learning Engineer
Learn about knok
At knok, we dare to lead and humanise the digital transformation of healthcare. We envision a world where everyone has timely access to quality healthcare through digital technology, creating a more equal society. We genuinely believe in it, and you can recognise it in every person who embraces this mission.
Through a Digital Front Door strategy, knok connects patients, providers and healthcare professionals in one place. Our API-first white-label platform enables a continuous, engaging and personalised healthcare experience for all conditions through a cutting-edge Patient Journey Engine.
With regular clinical practice as our main source of knowledge, we leverage ready-to-use data to improve care automation and increase financial savings. Since 2015, we have enabled more than 2.5 million clinical interactions in over 12 countries. Our platform is scalable and AI-ready, enhancing the power of data-driven care to deliver better outcomes during all stages of life.
Are you ready to join us in revolutionising healthcare and making a tangible impact on people's lives?
About the role
We are looking for a Machine Learning Engineer to join our team and build, maintain, and monitor ML-powered features that directly impact our platform and healthcare workflows. In this role, you will work closely with Data Engineering, Product, and Clinical teams to take initiatives from experimentation and evaluation through deployment, monitoring, and continuous improvement.
You will work at the intersection of data science and software engineering deploying robust and scalable systems built on healthcare data. This role requires strong coding and system design skills, sound ML foundations, and the ability to turn ideas into production-ready features.
If this makes sense, keep reading!
As a Machine Learning Engineer, you will:
- Contribute to designing, building, evaluating, shipping, and improving the product through AI/ML development.
- Work alongside the Product, Data and Engineering teams to implement AI/ML-powered features.
- Develop initiatives across the AI stack (prompt engineering, RAG, fine-tuning, agentic workflows), prototype fast, and push to production.
- Collaborate with Data Engineering team to develop the datasets required to build and evaluate models.
- Read research in the health AI space and translate research advancements into tangible product features.
About you
To be considered for this role, here are the skills we’re looking for:
- Bachelor's or Master’s Degree in Computer Science, Data Science, Mathematics, Physics, or a related field.
- 2+ years of professional experience as a ML engineer, applied researcher, or software engineer with a focus on ML systems.
- Experience taking applied ML initiatives from early experimentation and prototyping through evaluation, deployment, and iteration in production.
- Strong foundations in machine learning, including generative AI.
- Experience deploying ML/AI services in a cloud environment.
- Ability to communicate technical concepts to non-technical stakeholders.
Nice to have:
- Track record of building and deploying production-grade software in healthcare.
- Experience with model training and observability platforms (e.g., Vertex AI, SageMaker, WandB, MLFlow, LangSmith, Langfuse).
- Experience with vector databases (e.g., ChromaDB, Pinecone).
- Experience working with analytical databases (BigQuery, Redshift, Snowflake).
Recruitment stages
1. People Interview: A conversation to get to know you better, explore your background and motivation, and introduce you to knok, our culture, and the role.
2. Hiring Manager Interview: A short conversation with the Hiring Manager to discuss your data science background, research approach, technical experience, and problem-solving mindset.
3. Case Study: A practical exercise designed to understand how you structure a problem, approach model evaluation, and balance research with production readiness.
4. Final Interview: A conversation with the Hiring Manager and a member of our Senior Leadership Team, where you’ll present and discuss your case study, explain your technical decisions, and explore your approach to collaboration, ownership, and impact.
We know that great candidates don't always fit a rigid checklist. If you don't meet every single requirement but are passionate about AI and healthcare, we strongly encourage you to apply!