Systems Design Engineer, Bioengineering

About the Role

Thermo Fisher Scientific’s Genetic Sciences Division seeks an early-career Bioengineer to assist in developing next-generation genetic-analysis, multiomics, and automated scientific workflows. This is a hands-on systems role focused on building prototypes, designing experiments, integrating technologies, and using data to solve multidisciplinary problems. The successful candidate will contribute to system definition, architecture, integration, and verification across instruments, consumables, assays, software, and data.

This is a fully onsite role based at our Pleasanton, CA facility.

Key Responsibilities

• Support System Definition and Product Development:
• Collaborate with customers, scientists, engineers, and external partners to understand product and workflow needs.
• Translate customer and scientific needs into system requirements, specifications, and measurable performance targets.
• Maintain traceability among customer needs, system requirements, subsystem requirements, and verification evidence.
• Contribute to system architecture, interface definition, concept development, and technical trade studies.
• Evaluate candidate concepts based on scientific performance, feasibility, throughput, cost, usability, and risk.
• Support products from early feasibility through integration, verification, and design transfer.
• Build, Integrate, and Test Systems:
• Design and build prototypes integrating optical, electrical, mechanical, fluidic, biological, and software components.
• Develop test fixtures, automated experiments, and data-acquisition methods.
• Conduct system integration, testing, and troubleshooting of complex instrumentation and consumable workflows.
• Design experimental plans, perform studies, and analyze results using appropriate statistical methods.
• Investigate failures using root-cause analysis, DOE, FMEA, and structured problem-solving.
• Provide clear, data-driven recommendations through reports, presentations, and technical discussions.
• Contribute to design reviews, risk assessments, and product-development documentation.
• Apply Models, Data, and AI:
• Develop models for process behavior, throughput, performance, and system trade-offs.
• Apply MBSE practices to connect workflows, requirements, system behavior, interfaces, and verification.
• Use Python, MATLAB, R, JMP, or comparable tools for modeling, data analysis, and visualization.
• Apply machine learning and advanced analytics to identify patterns, anomalies, and system-performance drivers.
• Use AI-assisted tools to accelerate literature review, concept exploration, coding, experiment design, and technical analysis.
• Develop reproducible analysis pipelines with appropriate version control, documentation, and scientific review.
• Validate AI-generated results through independent analysis and experimentation.
• Connect Biology and Engineering:
• Evaluate interactions among samples, assays, reagents, consumables, instruments, and software.
• Support genetic-analysis and sample-preparation workflows such as qPCR, dPCR, sequencing, or multiomics.
• Help teams understand sources of biological and technical variability.
• Partner with assay, consumables, systems, automation, and V&V teams to improve performance and robustness.
• Ensure engineering decisions reflect complete customer workflows rather than isolated components.
• Contribute to the development of products for research-use and regulated markets.
• Advance Automation and Modern Engineering Methods:
• Integrate sensors, microcontrollers, instruments, liquid handlers, or robotics into experimental systems.
• Develop scripts and software tools for instrument control, automated testing, and data collection.
• Use CAD, simulation, rapid prototyping, and laboratory fabrication to evaluate concepts quickly.
• Apply model-based and automated methods to reduce development cycles and physical iteration.
• Explore AI applications in workflow optimization, anomaly detection, predictive modeling, and intelligent instruments.
• Introduce useful new tools and methods while learning from experienced product developers.

Qualifications

• Education and Experience:
• PhD in bioengineering, biomedical engineering, chemical engineering, biophysics, or a related field, with zero to approximately three years of relevant experience.
• Master’s degree in a related discipline with approximately two or more years of relevant research or industry experience.
• Bachelor’s degree in a related discipline with approximately five or more years of relevant product-development experience.
• Additional Qualifications:
• Hands-on experience developing experimental systems, prototypes, or engineering solutions for biological applications.
• Experience working across scientific and engineering disciplines.
• Industry experience is valued but not required when academic work demonstrates practical system development and problem-solving.
• Technical Capabilities:
• Strong foundation in bioengineering, molecular biology, biophysics, or biological-system development.
• Hands-on experience with several of the following:
• Molecular biology or sample-preparation workflows
• Microfluidics and fluid handling
• Optical, electrical, or mechanical instrumentation
• Sensors, data acquisition, and instrument control
• Robotics or laboratory automation
• CAD and rapid prototyping
• Computational modeling and simulation
• Statistics, machine learning, or AI
• Experience with systems-engineering practices, including requirements, interfaces, risk analysis, and V&V.
• Familiarity with DOE, RCA, FMEA, and statistical analysis.
• Proficiency with tools such as Python, MATLAB, JMP, R, LabVIEW, SolidWorks, or comparable platforms.
• Experience with qPCR, dPCR, CE/Sanger sequencing, microarray, NGS, DNA synthesis, or related workflows is preferred.
• Behaviors:
• Hands-on builder who learns through experiments, prototypes, and data.
• Curious across biology, engineering, software, and emerging technology.
• Highly data-driven and attentive to system-level effects.
• Comfortable working through ambiguity and complex technical problems.
• Builds credibility and collaborative relationships across disciplines.
• Communicates technical results clearly in writing and presentations.
• Moves quickly while maintaining scientific rigor and reproducibility.
• Uses AI ambitiously while validating results through sound engineering judgment.