AI Sr Staff Engineer

Originally posted 10 November 2026 by the employer.

About the role

You will work on AI systems that power semiconductor design workflows, leading the adaptation of foundation models to specialized engineering domains like hardware description languages, RTL design, and EDA workflows.

What you'll do

• Lead the adaptation of foundation models to hardware description languages, RTL design, and EDA workflows using supervised fine-tuning, LoRA/PEFT, and synthetic data pipelines.
• Research and implement post-training methods including preference optimization, iterative alignment, and reasoning enhancement using Monte Carlo Tree Search, process reward models, and trajectory-based supervision.
• Architect agentic systems capable of multi-step planning, tool calling, self-correction, and analytics over structured data sources like CSV, JSON, Parquet, and SQL, with sandboxed execution.
• Own the evaluation strategy for LLM and agent systems by building automated frameworks with LLM-as-judge pipelines, domain-specific benchmarks, pass@k metrics, and regression suites.
• Develop production ML monitoring to detect feature drift, data drift, and performance degradation using statistical tests like Kolmogorov-Smirnov, Wasserstein distance, and Jensen-Shannon divergence.
• Build scalable pipelines for dataset construction, distributed multi-GPU training on NVIDIA A100/H100 clusters, checkpoint evaluation, and experiment tracking using PyTorch, Hugging Face, DeepSpeed, and FSDP.

What you'll need

• PhD in Computer Science, Machine Learning, Electrical Engineering, or equivalent practical experience in applied AI research and production ML systems.
• 8+ years of hands-on experience building and shipping machine learning systems, with at least 3 years focused on LLMs, post-training, or agentic AI.
• Strong foundation in supervised fine-tuning, parameter-efficient methods, preference optimization, and reasoning-enhancement techniques including search-based inference and reward modeling.
• Proven ability to design and implement rigorous evaluation frameworks, including automated benchmarks, LLM-as-judge pipelines, and statistical validation of model improvements.
• Experience building production ML monitoring, drift detection, and retraining pipelines using statistical methods and data-quality tools.
• Track record of translating research into production systems, with evidence through publications, patents, or shipped products.

Skills: LLM, PyTorch, RTL design, supervised fine-tuning, EDA workflows

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