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Responsibilities
- Define the long-term technical roadmap for laboratory automation systems, integrating robotic sample handling, automated metrology instruments, and data acquisition pipelines
- Architect and own the end-to-end automation infrastructure for high-throughput materials characterization workflows, including optical, mechanical, and electrical property testing of wearable device materials
- Collaborate with scientists, hardware engineers, and product teams to translate experiments and lab workflows into clear integration specifications, data models, and scalable automation solutions
- Work with integrators and vendors to design, build, and commission automated workcells for materials R&D (process development, characterization, property testing, etc.)
- Build and maintain middleware services that connect instruments, robots, and sensors to laboratory information management systems
- Develop instrument drivers and automation scripts that generate command sequences and invoke vendor APIs/SDKs to orchestrate lab workflows end-to-end
- Collaborate with AI and data scientists to tightly integrate the autonomous lab with LLM-based multi-agent systems for experiment planning, analysis, and decision-making
- Design and implement data pipelines that capture, validate, and store experimental metadata to ensure data integrity and reproducibility across the discovery pipeline
- Evaluate and benchmark automation performance — measuring throughput, reliability, error rates, and turnaround time of automated experimental workflows
- Contribute to internal tooling, documentation, and best practices that enable the broader team to leverage automation capabilities
- Drive the adoption of design-of-experiments methodologies and statistical process control within automated materials screening workflows
- Define standards and best practices for automation system reliability, calibration, and data integrity across the materials research organization
- Provide technical guidance to other engineers on automation architecture decisions, instrumentation integration patterns, and software design for laboratory systems
- Evaluate and integrate emerging laboratory automation technologies, robotics platforms, and scientific instrumentation relevant to materials research
Minimum Qualifications
- Ph.D. degree in Electrical Engineering, Computer Science, Mechanical Engineering, Control Engineering, Materials Science, or relevant field, and/or equivalent practical experience
- 6+ years of experience in lab automation, systems integration, or industrial automation software and/or relevant technical experience
- Proficiency in Python, with experience writing production-quality automation and integration code
- Hands-on experience with lab automation platforms (e.g., liquid handlers, robotic arms, automated characterization tools)
- Experience with laboratory information management systems, electronic lab notebooks, or manufacturing execution systems
- Demonstrated ability to translate scientific or manufacturing workflows into reliable, automated processes
- Experience architecting scalable automation platforms for materials characterization or physical science research environments
- Experience with statistical analysis and data pipeline design for high-throughput experimental datasets
Preferred Qualifications
- A track record of commissioning or bringing up complex lab, pilot, or manufacturing equipment
- Familiarity with APIs, databases, and enterprise software integration patterns
- Experience defining automation strategy and technical standards at an organizational level within a research or advanced hardware development environment
- Familiarity with computational chemistry or materials science tools (DFT, MD, LAMMPS, ASE) and high-performance computing (HPC) environments
- Experience with retrieval-augmented generation (RAG), knowledge graphs, or scientific literature mining in the context of lab systems
- Publications or demonstrated accomplishments recognized in the field of laboratory automation or materials informatics
- Experience with materials relevant to wearables hardware, such as optical coatings, waveguide materials, display substrates, or flexible electronics
- Experience integrating robotic platforms with laboratory information management systems (LIMS) or material databases
- Experience integrating AI/ML models or LLM-based agent frameworks into physical lab workflows
- Experience with data historians, or real-time supervisory dashboards
- Knowledge of industrial communication protocols
- Familiarity with design-of-experiments frameworks and machine learning approaches applied to accelerated materials discovery
$184,000/year to $257,000/year + bonus + equity + benefits
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