AI Sr Staff Engineer

You will engineer the future with Synopsys, the leader in engineering solutions from silicon to systems, enabling customers to rapidly innovate AI-powered products. The base salary range for this role is $193,200-$289,800.

You have spent years building AI systems that have to actually work in production, not just hit benchmarks in a paper. The difference between a model that ships and one that gets shelved is usually in the evaluation framework, the data pipeline, or the post-training strategy, and you are the kind of engineer who catches those gaps before they become blockers.

At Synopsys, you will work on AI systems that power semiconductor design workflows. The problems are hard, the data is structured and verifiable, and what you build will be used by engineering teams building the chips that run the world.

Responsibilities

• Lead the adaptation of foundation models to specialized engineering domains like hardware description languages, RTL design, and EDA workflows, using supervised fine-tuning, LoRA/PEFT, and synthetic data pipelines that improve correctness and coverage
• Research and implement advanced 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 and privacy-preserving designs
• Own the evaluation strategy for LLM and agent systems, building automated frameworks with LLM-as-judge pipelines, domain-specific benchmarks, pass@k metrics, and regression suites that gate model releases
• 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 combined with feature importance
• 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
• Set standards for experimental rigor across the team, including strong baselines, controlled ablations, reproducibility, error analysis, and honest reporting of negative results, and translate outcomes into publications, patents, and production-ready designs

The Impact You Will Have

• Define the technical direction for domain-specific LLMs that generate verifiable hardware code, improving the productivity of engineering teams designing complex semiconductor systems
• Enable reasoning-enhanced models that solve multi-step planning and coding tasks with measurably higher correctness, reducing iteration cycles for engineers working in EDA workflows
• Deliver enterprise analytics agents that operate autonomously over heterogeneous structured data, automating analysis and reporting tasks that today require manual effort
• Build evaluation frameworks that become the standard for how the team measures quality, gates releases, and maintains trust in production AI systems
• Detect and remediate production ML drift before it impacts users, ensuring models remain reliable and accurate as data distributions shift over time
• Grow the technical capability of the team through mentorship, design reviews, and by setting the example for what rigorous, reproducible AI research looks like in practice
• Represent Synopsys AI research externally through publications and invention disclosures that advance the state of the art in domain-specific AI systems

Requirements

• 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
• Deep expertise in PyTorch, Hugging Face ecosystem (Transformers, PEFT, TRL, Datasets, Accelerate), and distributed training frameworks like DeepSpeed or FSDP
• 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 that demonstrate both depth and engineering discipline