Systems Design Engineer - Bioengineering

Work Schedule

Standard (Mon-Fri)

Environmental Conditions

Office

Job Description

Discover Impactful Work

Thermo Fisher Scientific’s Genetic Sciences Division develops integrated platforms, instruments, consumables and reagents for research and clinical applications.

We are seeking an early-career Bioengineer to help develop next-generation genetic-analysis, multiomics and automated scientific workflows. This is a hands-on systems role for someone who enjoys building prototypes, designing experiments, integrating technologies and using data to solve multidisciplinary problems.

The successful candidate will bring modern capabilities in computational modeling, automation, data science and AI. Working with experienced scientists and engineers, this person will contribute to system definition, architecture, integration and verification across instruments, consumables, assays, software and data.

Location

This is a fully onsite role based at our Pleasanton, CA facility. Relocation assistance is not provided for this position.

What You’ll Do

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 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.

What You Bring

Education and Experience

One of the following combinations:

• 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

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