PLEASE NOTE:

To provide the best candidate experience amidst our high application volumes, each candidate is limited to 10 applications across all open jobs within a 6-month period.

WHAT YOU BRING

• BS Computer Science or equivalent with a minimum of 10+ years or a MS in Computer Science with 8+ years or PhD 5+ years in Electrical Engineering/Computer Engineering/Applied Physics (or equivalent experience).
• Proficiency with SPICE/Spectre/HSPICE/Cadence Virtuoso/ADE; layout and parasitic extraction.
• Strong Python and ML fundamentals (PyTorch or TensorFlow, scikit-learn/NumPy/Pandas)
• Experience building and validating surrogate models and running multi-objective optimization.
• Experience with GNNs for layout-to-electrical, PINNs/physics-informed ML, or differentiable simulation.
• RL for control/param tuning; Bayesian optimization at scale; DoE/active learning.
• Publications/patents in display, EDA, or ML-for-hardware.
• Solid analog/digital circuit fundamentals; hands-on OLED pixel/gate driver design.
• Demonstrated ability to take designs from simulation to hardware bring-up and correlation.
• You’re inclusive, adapting your style to the situation and diverse global norms of our people.
• An avid learner, you approach challenges with curiosity and resilience, seeking data to help build understanding.
• You’re collaborative, building relationships, humbly offering support and openly welcoming approaches.
• Innovative and creative, you proactively explore new ideas and adapt quickly to change.
  

  LI-VL1

AI for design & optimization

• Develop physics-informed ML and GNN surrogates mapping layout/process to electrical KPIs.
• Run multi-objective optimization via Bayesian optimization/evolutionary search; generate candidate pixel, driver topologies.
• Apply RL for process control and compensation strategy tuning.

Circuit Simulation

• Compose fast design loops that combine Layout/SPICE with learned surrogates; accelerate what-if sweeps.
• Build data/feature pipelines from fab/test and panel bring-up; implement active learning and uncertainty quantification.