Senior Systems Engineer, R&D

Company Description

Guardant Health is a leading precision oncology company focused on helping conquer cancer globally through use of its proprietary tests, vast data sets and advanced analytics. The Guardant Health oncology platform leverages capabilities to drive commercial adoption, improve patient clinical outcomes and lower healthcare costs across all stages of the cancer care continuum. Guardant Health has commercially launched Guardant360®, Guardant360 CDx, Guardant360 TissueNext™, Guardant360 Response™, and GuardantOMNI® tests for advanced stage cancer patients, and Guardant Reveal™ for early-stage cancer patients. The Guardant Health screening portfolio, including the Shield™ test, aims to address the needs of individuals eligible for cancer screening.

Job Description

We are seeking a Senior R&D Systems Engineer to lead system design development and optimization using systems understanding, computational and data-driven approaches for novel NGS-based oncology diagnostic devices. This role is ideal for individuals who are curious, adaptable, and eager to apply innovative solutions to advance high-throughput sequencing automation for cancer diagnostics. Prior experience is valuable, but a drive to learn and continuously improve is essential.

As part of a multi-disciplinary technology development team, you will collaborate with Engineering (Hardware/Software), Assay Scientists, Bioinformatics, Quality, Regulatory, Service Engineering, and Operations to develop scalable, automated sequencing workflows that enable breakthroughs in cancer diagnostics. You will have the opportunity to contribute to the full lifecycle management of new products, from early development to clinical validation and commercialization, ensuring seamless integration of cutting-edge NGS technology into production-ready solutions.

Essential Duties and Responsibilities:

• Define, optimize and design system architecture for NGS-based cancer diagnostic devices, using data-driven approaches and computational methods to improve performance, scalability, and reliability.

• Collaborate with cross-functional teams (engineering, assay development, bioinformatics, and regulatory) to make design decisions and ensure smooth system integration for clinical use.

• Develop and apply testing strategies, including risk-based testing, process monitoring, and performance optimization, to ensure system robustness.

• Analyze system interactions and root cause for system level issues and failures.

• Prototype and implement new technologies to improve system reliability, automation efficiency, data connectivity and scalability.

• Apply Machine Learning techniques for longitudinal monitoring, computer vision systems, system optimization, and predictive insights to improve our automated workflows.

• Contribute to defining and evaluating requirements, as well as system verification and validation, ensuring compliance with regulatory standards and delivering high-quality automation solutions.

• Apply Generative AI to automate systems engineering product lifecycle workflows.  

• Participate in the full product lifecycle, from concept and feasibility to validation, manufacturing, and clinical launch.

Qualifications

• PhD/MS/BS in an engineering or related science discipline, such as Computational Science, Machine Learning Eng, Data Science, Biomedical Engineering, Mechanical/Electrical Engineering, Data Science, Computer Science, or related field.

• Experience in applying data-driven, computational, or automation techniques to solve complex engineering challenges.

• Ability to learn quickly and adapt to new technologies and approaches in automation and high-throughput sequencing for oncology applications.

• Proficiency in Python, SQL, and exposure to Machine Learning and Generative AI for enhancing workflows, automating tasks, and optimizing system performance.

• Proficiency in design control fundamentals.

• Willingness to contribute to full lifecycle management of oncology diagnostic devices, from concept through development, validation, and commercialization.

Nice-to-Have:

• Background in NGS assay automation, sample preparation robotics, or liquid handling automation.

• Interest in automation of Design Control workflows using Generative AI.

• Experience with data-driven decision-making, machine learning, and Generative AI to improve system performance.

Why Join Us?

• Design and implement cutting-edge automation solutions for the next generation of NGS-based oncology diagnostics.

• Work at the intersection of robotics, automation, and