Weekend Health Informatics Jobs in Morrison, CO (NOW HIRING)

Straddle is building the intelligence layer for modern payments—enabling smarter, faster, and more reliable financial decisions through data and machine learning. We operate at the intersection of fintech, data infrastructure, and real-time decisioning, where the systems we build directly impact transaction success, fraud detection, and customer experience.

We are a fast-moving, high-ownership team that values speed, clarity, and pragmatic execution. We believe in delivering impact quickly, iterating continuously, and building systems that scale as the business grows.

We are seeking a Senior/Staff ML/Data Platform Engineer to own the design and implementation of our data and machine learning platform.

This role spans data engineering, ML engineering, and MLOps, with responsibility for building a scalable lakehouse architecture, productionizing models, and enabling real-time and batch decisioning systems.

This is a hands‑on role requiring strong individual contribution across system design, coding, and deployment. The ideal candidate can balance speed and scalability, make pragmatic trade‑offs, and operate with high ownership in a fast‑paced startup environment.

• Design and build scalable data pipelines for ingesting and processing transactional and event data
• Architect and implement a Databricks-based lakehouse using Delta Lake and Unity Catalog
• Build and maintain feature pipelines and feature store infrastructure
• Deploy machine learning models in batch and real‑time environments
• Implement CI/CD pipelines for data and ML workflows within Databricks
• Set up model monitoring, drift detection, and automated retraining pipelines
• Design real‑time and batch processing architectures based on business needs
• Develop dashboards and analytics to monitor product, model, and business performance
• Manage and optimize data infrastructure, storage, and database systems
• Translate business problems into scalable data and ML solutions
• Collaborate cross‑functionally with data science, engineering, and product teams
• Continuously improve system performance, scalability, and reliability

• 5+ years in data engineering, ML engineering, or related roles
• Strong experience building production‑grade data pipelines (ETL/ELT)
• Proficiency in R/Python and SQL
• Experience with Databricks and Apache Spark
• Experience with cloud platforms (preferably Azure)
• Experience deploying ML models into production systems
• Familiarity with CI/CD, containerization (Docker), and DevOps practices
• Experience with ML lifecycle tools (e.g., MLflow, Kubeflow, Vertex AI)
• Strong problem‑solving and debugging skills
• Ability to work across ambiguous, evolving requirements
• Strong communication and collaboration skills

• Databricks ecosystem (Delta Lake, Unity Catalog, MLflow)
• Data modeling, warehousing, and lakehouse architectures
• Feature engineering and feature store design
• Batch and real‑time data processing (e.g., Spark, Kafka, streaming systems)
• REST APIs / microservices for model serving
• Data quality, observability, and monitoring frameworks
• Security and compliance for sensitive financial data

• Speed over perfection — momentum creates opportunity; we deliver, iterate, and improve
• Ownership mentality — we don’t stop at “our part”; we ensure outcomes
• Honest, data‑driven thinking — we trust the data, even when it’s inconvenient
• Curiosity and creativity — we ask “why,” explore ideas, and challenge assumptions
• Pragmatic execution — we balance long‑term scalability with immediate business impact
• Collaborative mindset — we think out loud, share context, and make each other better

We are building systems that directly impact real financial outcomes. That responsibility demands high standards, strong judgment, and a bias toward action.

Apply Now

Apply now

Your application will be reviewed by us. We’ll get back to you quickly. We can’t wait to meet you!

Flexible Work Environment – We offer hybrid and remote options so you can work where you’re most productive, whether that’s at home, in‑office, or a mix of both.

Equity Ownership – As an early team member, you’ll receive equity in the form of options or RSUs—your contributions grow the company, and you share in the upside.

Unlimited PTO – Take the time you need to rest, recharge, or handle life outside of work. We trust our team to balance time off with results.

Health & Wellness Coverage – Comprehensive medical, dental, and vision plans help keep you and your family healthy, with 100% employee premium coverage on select plans.

Paid Parental Leave – We support growing families with fully paid time off for new parents, including adoption and foster care.

Professional Development – We invest in your growth with paid courses, certifications, and conference opportunities tailored to your role and interests.

Home Office & Equipment Stipend – Receive a stipend to set up your home workspace and get the tools you need to work comfortably and effectively.

Team Retreats – We host regular offsites to align on strategy, collaborate face‑to‑face, and have fun as a team.

Autonomy & Ownership – We give you space to lead initiatives, own outcomes, and shape the direction of your work without micromanagement.

Mission‑Driven Work – Help build infrastructure that moves money more efficiently, securely, and transparently for modern businesses.

Contact us to learn more about what we can help you build – or create an account to get started right away.

Seasoned engineers who thrive on shipping secure, scalable payments infrastructure. You’ll design APIs, review code, automate tests, and own services end‑to‑end in a cloud‑native stack.

Bridge the gap between prospects’ technical questions and Straddle’s platform. You’ll own demos, prototype integrations, and guide customers through launch while shaping the product with real‑world feedback.

Own the pipelines that move, transform, and operationalize data for analytics and machine learning. You’ll design resilient ETL jobs, manage feature stores, and collaborate with data scientists to productionize models.