Applied AI Engineer

Position Overview

We are seeking highly skilled Applied AI Engineer (Software Engineer) to build intelligent systems that automate, optimize and validate PCB design workflows.

The person will work at the intersection of electronics engineering, EDA tools and AI to significantly reduce design cycle time, improve quality and enable next‑generation autonomous PCB design capabilities.

The role involves working with large-scale datasets, reinforcement learning, optimization, algorithms, building predictive and deploying ML/AI solutions for complex PCB Design workflows.

What You Will Do

• Collect, clean and preprocess structured and unstructured data from multiple sources (EDA software etc.)
• Build working prototypes for AI-assisted PCB Design automation.
• Design, train and evaluate supervised, unsupervised and RL (reinforcement learning) machine learning models.
• Implement models such as regression, classification, clustering, time series, GNNs, reinforcement learning and optimization algorithms
• Formulate automation task as an optimization/RL problem, including state representation, action space, reward design, constraints and evaluation criteria.
• Evaluate and implement different algorithms such as simulated annealing, greedy approach, graph-based, force-directed methods, constraint solving, ILP/CP-SAT or evolutionary algorithms.
• Develop RL or learning-guided methods using realistic EDA/PCB design data.
• Define quality metrics for evaluating layout or assignment solutions based on cost efficiency, design-rule violations, conflict minimization, density and engineering review effort.
• Create benchmark datasets and evaluation pipelines to compare generated output against baselines and engineer-reviewed layouts.
• Design data representations for components, nets, board regions, keep-out zones, mechanical boundaries, constraints and connectivity graphs.
• Build visual/debug tooling to inspect output, failure cases and quality metrics.
• Work with PCB/layout/domain experts to translate design rules and PCB Design practices (placement, routing etc.) into software constraints.
• Contribute to the path from research prototype to usable engineering workflow.
• Ensure AI solutions follow ethical, responsible and explainable AI practices

Required Qualifications /Skills

• 1-3 years of hands-on software engineering applied ML, optimization, robotics planning, EDA automation, CAD automation or related experience.
• Strong Python programming.
• Hands-on experience with PyTorch, JAX, TensorFlow or similar ML frameworks.
• Practical reinforcement learning experience beyond tutorials, including environment design, reward shaping, training loops, evaluation, and debugging.
• Strong fundamentals in algorithms, graph methods, search, combinatorial optimization, computational geometry, or constraint solving.
• Experience solving structured optimization problems such as placement, routing, scheduling, packing, assignment, layout, planning, or path optimization.
• Ability to independently build prototypes from problem formulation through implementation and evaluation.
• Experience designing experiments, metrics, benchmarks, and reproducible evaluation pipelines.
• Strong debugging, testing, profiling, and code-structuring skills.
• Ability to collaborate with domain experts and convert engineering rules into algorithmic constraints.

Good To Have

• PCB placement, PCB layout automation, EDA routing/placement, VLSI physical design, CAD/CAM automation or design automation experience.
• Experience with ECAD/EDA tools such as Cadence Allegro, Altium, Siemens/Mentor, Zuken, KiCa, or similar.
• Experience with graph neural networks, imitation learning, offline RL, actor-critic methods, policy-gradient methods or hybrid RL + heuristic systems.
• Experience with OR-Tools, CP-SAT, ILP/MIP solvers, simulated annealing, genetic algorithms, Bayesian optimization or other metaheuristics.
• Experience with graph/netlist data, geometric layouts, spatial optimization or constraint-heavy engineering data.
• Experience with Ray/RLlib, Stable-Baselines3, CleanRL, Gymnasium or custom RL environments.
• GPU training, distributed experimentation, experiment tracking or scalable model evaluation experience.
• Bachelors/master’s in computer science, Electrical Engineering, Robotics, AI/ML, Applied Mathematics, Operations Research, or related field.

Ideal Candidate Backgrounds

• Senior ML engineer with real reinforcement learning or combinatorial optimization experience.
• Optimization engineer from robotics, scheduling, logistics, CAD/CAM, GIS, EDA, or spatial planning.
• EDA/VLSI/PCB automation engineer with strong software and optimization skills.
• Applied researcher who has shipped or prototyped working systems beyond academic experiments.
• Software engineer who has built scalable experimental systems for structured optimization problems.