The role
- COMP
- $140K - $160K
- EQUITY
- None
- LOCATION
- Texas
- WORKPLACE
- On-site
- EXPERIENCE
- 5 - 15 years
- VISA
- None
- INDUSTRY
- Hardware, Manufacturing, AI
/roles — ROLE_167
Sr Technical Project Manager - Equipment Engineering
Established AR hardware company scaling production of its optical waveguides from lab to high volume
The company
Long-established augmented reality company that designs and manufactures AR displays and optical components, including lightweight waveguides made in volume, and supplies partners building AR products.
- STAGE
- public-scale
- FUNDING
- public-scale
- TEAM
- 800+ people
- BACKING
- VC-backed
JD — the work
About the role
You would own the scale-up of the company's proprietary AR waveguide manufacturing, taking processes proven in R&D from small validation batches to high-volume production. The role connects research, process and equipment engineering, and factory operations, and you are accountable for results across the whole line rather than a single step. It calls for someone who owns delivery outright, digs into technical detail, and uses AI tools as a real part of daily work. The role is on-site in Texas.
What you'll do
- Own new product introduction for waveguide production, scaling from small proof runs to very high wafer volumes
- Drive output, yield, and quality metrics across the full manufacturing line
- Align research, process, equipment, and volume-production teams so scale-up programs stay on schedule
- Keep technical teams accountable and decide, with engineers, when an issue is resolved well enough to proceed
- Juggle four or five concurrent programs that turn newly invented processes into production-ready ones
What they're looking for
- 5+ years managing technical programs in chip fabs, optics, or other precision manufacturing
- Owned end-to-end process scale-up from development into high-volume production, rather than one isolated module
- Hands-on time in fabs, cleanrooms, or similar controlled manufacturing environments
- Real accountability for outcomes, owning results rather than reporting on them
- Enough technical depth to question equipment and process specialists and make sound calls
- Daily, practical reliance on AI tools for real, usable work output