tcpekin

Zeiss SMT is the leader in extreme engineering optics for semiconductor manufacturing. We come up every so often on HN as leaders in EUV optics manufacturing for ASML scanners. SMS is a branch of SMT concerned with standalone tooling for quality assurance and defect inspection in semiconductor fabs (tools like the AIMS EUV or PROVE systems, for example). We bring complete solutions to the hypercompetitive market that is the semiconductor industry. The scale is vast – the work you can be involved in ranges from simulations at the nanometer to how to ship tools via using Boeing 747(s).

Software (and extremely diligent/precise engineering… honestly it blows my mind regularly) underpins everything we do and spans the same vastness of scale. We (Zeiss SMT, but also SMS where I work) are hiring in pretty much every IT field as we modernize systems, upgrade legacy code, and build cutting-edge new tools and platforms to remain at the front of the field. Specifically interesting to HN (I think) is my team, which works on developing extremely fast physics simulation and data processing code utilizing classical algorithms, machine learning, and _AI_, all on premise. We also build and maintain the hardware (HPC clusters) to enable these developments.

Desirable skills Python (scientific, HPC, ML, physics simulation), image analysis (big, big data processing, very fast), CUDA, C++, HPC architecture design/on premise rack configuration, HPC admin, DevOps infrastructure (physical as well as CI/CD), GPGPU programming, FPGA programming, QA engineering, Kubernetes

I can post some job links tomorrow, but if you have questions please either comment or reach out to me at thomas dot pekin at zeiss dot com. I’ve been working here for a year and it’s a lot of fun.

Comment · 2 years ago

Grad students at Cal cost a professor ~100k a year, and then leave after 2-5 years with any optimizations they might have personally made. They also only work 6-12 hours a day, and having been said grad student, get mind numbingly bored after about 10-15 repetitive syntheses, spending lots of time on them, when the (only) interesting part, is the XRD pattern at the end... I would have absolutely advocated for such an arm if I was still there.

Comment · 3 years ago

The others might move wafers 24/7 but they don't have three shifts of R&D scientists and engineers working to continuously analyze the results/improve yield/performance 24/7. TSMC does.

Comment · 3 years ago

I worked as a grad student in LBL/UC Berkeley for 5 years. At the time I didn't want to make a career of it, but if I had to go back to a national lab, that might be the one. The culture was department dependent, but at the Molecular Foundry was pretty good. The campus is gorgeous, it's not isolated like other national labs at all, pay is low compared to tech for the Bay Area, but not pennies, and the conversations and people you can meet are fantastic. Always lots of new faces with students and visitors, but the core group that I interacted with were all very kind, helpful, hardworking, and just fun to talk to about science! I can definitely recommend it.

Comment · 3 years ago

I am 99% sure I know him - shares your same first initial? Tell him hello from me (TC Pekin), I always liked talking to him during some down time, and the feeling was mutual. As a grad student I always liked talking to him and hearing about all the history and his time at NCEM!

Comment · 4 years ago

I've made a website that does very rudimentary simulation of transmission electron microscopy diffraction patterns, as a tool for myself and other researchers. Was a big leap for me learning how to do so, but it's been fun (and frustrating) to learn about Flask, Docker, cloud instances, HTTPS etc. along the way!

https://tcpekin.com/dp_sim/

Comment · 4 years ago

I agree with this, my alcohol consumption has really tailed off after I started training for a marathon. It is impossible for me to actually train hungover and the required amount of training just so you can finish is 4-5x a week.

Comment · 4 years ago

I'm a researcher, so I do move/access a lot of data locally (10s of Gbs, not Tbs usually). Lots of Jupyter/IPython interactive workflows. Starting to run some more web development stuff/Docker containers.