Example Company
Software engineer
- Location
- San Francisco
- Type
- Full time
- Compensation
- $150k-$200k
- Posted
- 2 days ago
Join a team building useful software. Work closely with engineering and design to turn new ideas into products.
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Photonic Product Engineer for Analytics and ML
- Type
- Full time
- Posted
- 8D AGO
About the role
Nokia is searching for a qualified candidate to work as a Product Engineer focused on data engineering, machine learning (ML), automation, and analytics supporting our coherent optical pluggable product lines. The candidate hired to this position will help the wider RnD and Manufacturing teams by applying ML or other analytics to the data we generate to detect line trends sooner and uncover correlations that were previously not seen. Working in a vertically integrated organization, there will be opportunity to help products achieve industry leading performance through engineering optimization end-to-end. The position is based in Allentown, Pennsylvania.
Position Requirements
M.S. or Ph.D. in Electrical Engineering, Photonics, Applied Physics/Mathematics, Computer Science or Data Science, with 3+ years of experience applying data analytics or ML (Ph.D. can count toward the experience).
Strong Python skills, data analysis, and data visualization skills
Experienced applying machine learning algorithms to large data sets and evaluating algorithm efficacy
Familiarity with communications systems concepts is a plus
Solid statistics background (regression, hypothesis testing, Design of Experiments, Statistical Process Control).
Strong SQL skills on large datasets.
Comfortable presenting data, making proposals to technical audiences, and working in a highly cross-functional environment.
ractices within the group
Develop, improve, and maintain data visualization platforms to enable faster detection of line trends
Mine large manufacturing and test datasets, from wafer through module, to find yield drivers and failure signatures.
Deploy ML algorithms and AI agents to improve our ability to uncover correlations in the large data sets generated by the manufacturing line, spanning chip to module, including anomaly detection, yield prediction, root-cause analysis and early detection of line drift.
Perform statistical data analysis to characterize and help improve product yields and validate ML algorithm outputs
Identify opportunities for automation of analyses and engineering tasks using GenAI and workflow automation, working with other AI/ML partners to help drive best practices within the group
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