Machine Learning Engineer

Join us at Diagnostic Robotics, where we empower healthcare providers, payers, and value-based care teams with unprecedented insights and automation. Our futuristic tools predict adverse health events and help care teams avoid them, all while streamlining workflows, easing administrative burdens, and automating mundane tasks. With systems trained on tens of billions of data points from claims and other data combined with decades of research-based clinical expertise, we exist to reduce healthcare costs, make care teams more effective, and save lives.

What you’ll be doing:

You’ll work and collaborate closely with the team’s product manager, data engineers, full stack developers, medical physicians, client-facing teams, and directly with clients. You will be the E2E owner of our data science and machine learning products. This includes maintaining and improving our ML pipelines, driving data-driven insights, and building new AI and analytics products.

You'll be taking ownership of your projects from ideation to productization.You’ll be expected to contribute not just as a technical expert, but as a partner to the product team, bringing your deep understanding of data to help shape business strategy and deliver valuable solutions.

We strongly emphasize autonomy, and default to hiring Managers of One (https://signalvnoise.com/posts/1430-hire-managers-of-one). You’ll also be expected to help other engineers succeed.

As you join our core team, you will have the opportunity to revolutionize every facet of our dev organization. From igniting a vibrant culture to shaping the very projects we undertake and revolutionizing our approaches, your influence will be immense.

If you want to dive deeper into what being an engineer in our engineering team means, let’s talk.

Key Responsibilities:

End-to-End ML Product Development: Design, develop, deploy and maintain ML solutions from concept to production. You’ll be responsible for end-to-end ownership, including data preparation, feature engineering, model selection, training, and deployment.
ML Pipeline Development: Build scalable and reliable ML pipelines using orchestration tools, you’ll manage the automation of model training, evaluation, and deployment.
Data Analysis and Insights: Perform data analysis tasks utilizing SQL and pandas to work with large datasets and generate key findings for stakeholders.
Collaboration with the Product Team: Work closely with cross-functional teams, including product managers, engineers, medical physicians, and business leaders, to define features and products, and deliver solutions that align with business goals.
Software Engineering Best Practices: Apply strong software engineering principles in your work, ensuring that the code is clean, maintainable, and follows industry best practices. Participate in code reviews, testing, and continuous integration.
Knowledge Sharing and Mentoring: Act as the data expert in the company, helping others to understand the power of data science and machine learning and mentoring team members as needed.
Stay Current with Emerging Technologies and Healthcare initiatives: Leverage new advancements in AI/ML, with a focus on generative AI, to ensure our solutions remain cutting-edge.

Requirements

Key Requirements:

Experience:
At least 6 years of software development experience (either in machine learning engineering or other software engineering disciplines).
At least 3 years of experience with machine learning engineering, with a proven track record of deploying ML models to production and working with large-scale datasets.
Technical Skills:
Strong experience building ML pipelines using tools such as Argo Workflow, Airflow, or similar.
Proficiency in SQL for data extraction and analysis. Experience with other data processing tools (e.g. Pandas, Dask, PySpark) is a plus.
Solid experience with machine learning frameworks (e.g. Scikit-learn TensorFlow, PyTorch).
Familiarity with cloud platforms (preferably AWS) and containerization technologies (Docker, Kubernetes).
Strong software engineering skills, including version control (Git), testing, and CI/CD practices.
Bonus if you have experience with Generative AI (e.g., GPT models, transformers, diffusion models) or other advanced AI techniques.
Soft Skills:
Strong communication skills with the ability to explain complex technical concepts to non-technical stakeholders.
Passion for healthcare and business: You should be driven to understand the business needs of the company and how data science can add value.
A growth mindset with a desire to continuously learn and stay updated with the latest trends in AI machine learning and healthcare.

Bonus Points:

Experience with cloud-native ML frameworks and deployment.
Familiarity with healthcare data (e.g. HIPAA, HL7, FHIR).
Experience with MLOps principles and tools for model monitoring, retraining, and versioning.