Decision Support Tools Engineer

The Opportunity

Abbott New Medical Sensors is an innovative and exciting new venture, investigating and commercializing novel technologies that will provide patients, and the professionals who treat them, with better health insights across a broad range of disease states. Our breakthrough wearable technologies support medical decision making and encourage patient-centric care with the combined aim of improving medical outcomes.

Although we are part of a Fortune 500 healthcare company, we embrace the entrepreneurial spirit of a start-up and are not afraid to challenge the status quo. We are preparing for significant growth in new areas of health and healthcare and are assembling a best-in-class team to take us into this new future.

We are seeking a high-calibre individual with a background in decision support algorithms and tools development to be part of Abbott New Medical Sensors team.

This is an onsite opportunity based out of Alameda, CA.

What You’ll Work On

• Develop and maintain automation scripts and pipelines that accelerate the design, testing, and deployment of decision support algorithms for continuous physiological sensor platforms (e.g., CGMs).
• Build interactive visualization tools for algorithm performance analysis, trend qualification, alert concordance, and clinical feasibility review.
• Design and implement Design of Experiments (DOE) matrices to systematically evaluate algorithm parameter spaces, threshold configurations, and edge-case scenarios.
• Leverage enterprise AI tools and AI sandboxes (e.g., Copilot, Claude, internal LLM platforms) to rapidly prototype, iterate on, and scale internal tooling.
• Create reusable toolkits and templates that enable algorithm developers and clinical scientists to run analyses, generate reports, and compare algorithm variants without manual intervention.
• Support data wrangling and formatting workflows — transforming raw sensor data exports into structured inputs for algorithm evaluation, without owning the algorithm science itself.
• Collaborate cross-functionally with data scientists, statisticians, and regulatory/quality teams to understand tooling needs and deliver fit-for-purpose solutions.
• Document tools, workflows, and standard operating procedures to ensure reproducibility and enable team adoption at scale.
• Continuously identify opportunities to reduce manual effort, improve turnaround time, and increase rigor in algorithm development workflows.

Required Qualifications

• Bachelor’s degree in engineering, Computer Science, Biomedical Engineering, or a related technical field.
• 1–3 years of professional experience.
• Proficiency in Python for scripting, automation, and data manipulation (pandas, NumPy, or similar).
• Demonstrated experience building data visualizations (Matplotlib, Plotly, Streamlit, or equivalent).
• Familiarity with version control (Git/GitHub) and collaborative development workflows.
• Comfort working with tabular/time-series data formats (CSV, JSON, structured sensor data).
• Hands-on experience using generative AI tools (GitHub Copilot, ChatGPT, Claude, or similar) as part of a development workflow — not just casual use, but as an integral productivity accelerator.
• Strong problem-solving mindset with the ability to independently scope, build, and deliver small tools end-to-end.
• Excellent written and verbal communication skills.

Preferred Qualifications

• Experience in a regulated industry (medical devices, pharma, diagnostics) or awareness of design control / V&V concepts.
• Familiarity with Design of Experiments (DOE) methodologies or statistical experiment design.
• Exposure to continuous sensor data (CGM, wearables, or similar physiological signals).
• Experience building lightweight web apps or dashboards (Streamlit, Dash, Flask, Gradio) for internal stakeholders.
• Familiarity with CI/CD pipelines, task runners, or workflow orchestration (GitHub Actions, Makefiles, Airflow).
• Experience with prompt engineering and integrating LLM APIs into automated workflows.
• Exposure to cloud or sandbox environments for prototyping (Azure, AWS, or enterprise AI platforms).
• Demonstrated ability to learn new tools and frameworks rapidly and independently — a "builder" mentality over a "researcher" mentality.
• Portfolio, GitHub profile, or examples of tools/scripts built for internal process improvement.

The base pay for this position is $90,000.00 – $180,000.00. In specific locations, the pay range may vary from the range posted.