ML Engineer

Navina is a fast-growing digital health SaaS company that’s on a mission to transform the way physicians interact with patient data. Thousands of clinicians across the United States already use Navina’s AI-powered solution that transforms complex and fragmented patient data into concise “patient portraits" and actionable clinical insights at the point of care. With Navina, physicians experience less burnout, reduce missed diagnoses, and can devote more time giving better care to their patients.

Navina has been named one of the Top 100 AI companies globally by CB Insights and made the list of the Top 50 Digital Health startups. We are already working with industry-leading value-based organizations including Privia Health and Agilon.

We're looking for talented, versatile and highly independent Machine Learning Engineers to join our winning team. You will be in charge of end-to-end development and you'll have a massive impact on the product and the technical decisions made.

The ML Engineers are part of our infrastructure group, working closely with the exceptional research team, taking trained models and scaling them out to production.

Requirements:

4+ years of hands-on experience in software engineering (Backend preferably in Python).
2+ years of experience in machine learning pipelines on cloud environments.
Knowledge in statistics and machine learning techniques.
Proven ability to lead product feature development, from concept to production.
Experience with large scale, high performance, production environments.
Experience working with SQL and NoSQL databases.
Experience in AWS cloud environment.

Advantages:

Bs.c / Ms.c in Computer Science / Software Engineering.
Experience with ML Frameworks such as PyTorch, TensorFlow and MLFlow.
Experience with Deep Learning, NLP and LLM pipelines (RAG and agentic systems).

You don’t need to meet 100% of the requirements to be a great fit. We believe in hiring people, not just checklists, and we value potential as much as experience. If this role excites you, we’d love to see your application!

Responsibilities:

Be responsible for the entire algorithmic lifecycle - from models training and evaluation infrastructure to implementation in production, monitoring and maintenance.
Help in creating scalable solutions by enabling us to continuously increase the accuracy of our algorithms across thousands of clinics.
Designing a secured large-scale system that is suitable for sensitive patient data.
Continue to enhancing our deep learning infrastructure to supports our AI models at scale, including CI/CD, automation, testing and monitoring.
Collaborate with the research, medical and product teams in implementing ML solutions to the digital health space.