Description

The Role

Our team is dedicated to empowering clinicians through data-driven innovations. We combine rigorous data science with practical engineering to build systems that make daily workflows more efficient, insightful, and intuitive. Our responsibilities span everything from data ingestion and transformation to advanced ML model deployment—ensuring clinicians have the right information at the right time to deliver exceptional care.

We thrive on curiosity, collaboration, and continuous learning. By working closely with Product, Design, and Engineering, we aim to create solutions that genuinely enhance clinician experiences. If you love tackling challenging problems and turning data into meaningful outcomes, you’ll find a welcoming and dynamic environment here. 

As a Senior Machine Learning Engineer, you’ll be at the forefront of using data to shape and optimize clinician workflows. Your day-to-day will blend creative problem-solving with hands-on technical work—designing experiments, building robust models, and collaborating with cross-functional teams to bring new ideas to life. You’ll be instrumental in guiding data initiatives that drive our product roadmap, helping us create intuitive features and tools that clinicians rely on every day.

You’ll also have plenty of chances to sharpen and share your expertise. We value mentorship, open communication, and pushing the boundaries of what ML can do in a real-world healthcare context. Whether you’re fine-tuning a model, presenting insights to stakeholders, or brainstorming new product features, your work will have a direct and meaningful impact.

Key Responsibilities

• Develop & Deploy ML Pipelines
  • Build end-to-end solutions—from data exploration to model deployment—that enhance clinician experiences
    
  • Optimize and maintain models for performance, reliability, and long-term scalability
    
• Lead Advanced Analysis
  • Conduct deep-dive analyses, uncovering insights that drive product decisions
    
  • Collaborate with product teams to turn data into actionable next steps
    
• Cross-Functional Collaboration
  • Work closely with Engineering, Product, and Design to ensure ML features are intuitive, impactful, and aligned with clinician needs
    
  • Communicate complex results clearly to both technical and non-technical partners
    
• Mentor & Advocate Best Practices
  • Guide less experienced team members, sharing knowledge on model development, MLOps, and data engineering
    
  • Champion a culture of experimentation, continuous learning, and proactive problem-solving
    
• Drive Innovation
  • Stay current with emerging ML tools and technologies, integrating new techniques that elevate our product capabilities
    
  • Look for creative ways to leverage data to make clinicians’ lives easier, more efficient, and more effective