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Lila Sciences is building Scientific Superintelligence to accelerate discovery across medicine, materials, and energy. They combine advanced AI models with proprietary instruments into an operating system for science that executes the scientific method autonomously.
You will be the human-factors voice for the Robotics team, working on software that runs Lila's autonomous labs. Partnering with software engineers and researchers, you'll make demanding surfaces—simulation, research operations, orchestration, and SCADA-style monitoring—clear, safe, and trustworthy under pressure, directly shaping factory speed and error rates.
Key responsibilities include:
- Lead usability research through contextual inquiry, task analysis, and usability testing to ground UI decisions in real workflows
- Design for high-stakes displays: shape information architecture and interaction patterns for high-density orchestration and SCADA-style monitoring where situational awareness is paramount
- Build Figma prototypes and testable flows for simulation, lab, and capacity-planning UIs, then iterate from evidence
- Design human-in-the-loop workflows defining how operators supervise, intervene in, and trust automated and AI-driven processes
- Embed with software engineers, translating research into specs, patterns, and design-system contributions
- Champion accessibility standards as the platform grows
Required qualifications:
- 4–8 years in UX research or human factors for complex operational, industrial, or scientific software (research-first role)
- Experience designing high-density, high-stakes interfaces such as control rooms, monitoring dashboards, or data-heavy tooling
- Core research methods: contextual inquiry, task analysis, usability testing
- Strong information-architecture and interaction-design skills with Figma or equivalent prototyping
- Understanding of human-in-the-loop design and situational awareness for operator supervision and error prevention
- Cross-functional communication and ability to defend design decisions with evidence
Bonus qualifications include human factors in regulated, safety-critical domains (aerospace, manufacturing, healthcare); laboratory or robotics context; SCADA and control-system familiarity; quantitative research methods; and experience designing AI-driven interfaces.
