Founding Member of Technical Staff- ML
Gemstar Staffing Group is looking for a Founding Member of Technical Staff- ML for an onsite position located in Palo Alto, CA.
Please send us your resume on our contact us page if you meet the skills below.
The Client: The client is building AI agents that automate semiconductor design flows. The client is building AI agents that shrink chip design from 3 years to 6 months. They just raised $4.5M pre-seed to solve the biggest bottleneck in chip design using AI.
Location: Palo Alto, CA (Onsite)
Salary Range: $180k -$320k base
Benefits: Usual FTE benefits + Equity
Visa Sponsorship: Can sponsor O1 and H1B visas
We’re looking for a founding ML engineer to push the frontier of AI in semiconductors. This is not a standard ML role, you’ll be building and shipping state-of-the-art models, reinforcement learning systems, and multi-agent architectures that directly operate on real-world RTL, chip architecture, and physical design workflows. You’ll be one of the first engineers, shaping the technical foundation of a company tackling to solve one of the missing piece in the AI ecosystem.
What You’ll Do
Train, fine-tune, and evaluate cutting-edge models for RTL, architecture, and PD tasks.
Design and deploy multi-agent systems for RTL code generation, design verification, and PD automation—leveraging MCP servers, toolchains, and real chip workflows.
Own the entire ML lifecycle from raw data pipelines to live deployment inside our production grade platform for chip engineers.
Collaborate cross-functionally with hardware engineers, product, and design to build AI-first chip design workflows.
Experiment at the frontier with LLMs, diffusion models, RL (especially test-time scaling), and graph-based approaches for EDA.
What We’re Looking For
Deep expertise in reinforcement learning, multi-agent systems, or large-scale model training—with a track record of shipping systems, not just papers.
Strong engineering skills in Python + ML frameworks (PyTorch, JAX, TensorFlow) and distributed training/inference.
Ability to move fast, prototype, and scale research into production.
Obsession with pushing state-of-the-art performance in real-world constraints.
Qualifications:
2+ years of experience of industry experience, with a strong preference for candidates with 4+ years. Worked at a frontier lab like Deepmind, OpenAI etc or at a fast moving deep tech startup like cursor, windsurf, cognition etc.
End-to-end design and deployment of RL systems for complex, real-world domains
Scaling LLMs, diffusion models, or GNNs from research to production.
Applying ML to RTL, synthesis, verification, or physical design workflows.
Experience at a fast-moving AI startup or AI division.
Graph Neural Networks (GNNs) & Code Models
PhD degree in Computer Science, Mathematics, or EECS with specialization in Machine Learning or Artificial Intelligence.
Coursework or Specialization in Reinforcement Learning & Control
Coursework in Distributed Systems & High-Performance Computing
Preferred Qualifications:
Experience applying ML to semiconductors or EDA (RTL, synthesis, PD).
Prior work at frontier AI or research labs or AI-driven chip companies or Background in graph neural networks, code models, or symbolic + neural hybrid systems.
Understanding how to scale ML training and inference on distributed infrastructure. Training large models efficiently requires knowing how to optimize workloads across GPUs, TPUs, or custom accelerators. Academic or practical exposure to HPC concepts, parallelization, scheduling, cluster optimization, will directly translate to our engineering challenges.
Background in Digital Logic Design / VLSI (for EDA Focus)
Proficiency in Python and modern ML frameworks (PyTorch/TensorFlow)
Familiarity with cloud computing systems and distributed systems.
Please send us your resume on our contact us page if you meet the skills above.