Machine Learning Engineer in Menlo Park, California at Voltai

About VoltaiVoltai is the leading AI company building agentic systems and frontier foundation models for semiconductor and electronics design. Backed by Sequoia Capital, we’re putting AI in the hands of hardware engineers in over 70% of the world’s largest semiconductor and electronics companies to have effortless control over their next-generation chip and board designs, powering the future of automotive, industrial automation, consumer electronics, IoT, and semiconductor manufacturing.
About the TeamOur founding team consists of IOI/IPhO olympiad medalists, Stanford professors, ex-CTO of Synopsys, and our business leadership has scaled revenue in their previous companies to over $1.5bn. At Voltai, we are combining the world’s best talent in the intersection of software and hardware.

Key Responsibilities

• Architect and develop high-performance AI systems that combine LLMs, retrieval pipelines, and agentic frameworks tailored to semiconductor and electronics design tasks
• Curate, manage, and optimize large-scale training and evaluation datasets, leveraging both synthetic and human-collected data to support foundation model development
• Train, fine-tune, and deploy foundation models, optimizing for latency, accuracy, and cost across diverse deployment scenarios
• Design cutting-edge retrieval and search algorithms for use in engineering documentation, design schematics, datasheets, and other technical corpora
• Build robust evaluation pipelines to measure model performance across tasks such as code generation, schematic synthesis, and long-context reasoning
• Translate SOTA research into production-ready code, working across the full ML stack from paper to GPU.
• Own strategic technical initiatives, collaborating with customers, engineers, and researchers to solve domain-specific problems with measurable impact

Required Skill Sets

• Strong programming expertise in Python, C, or Rust, with a focus on writing performant and maintainable code for large-scale AI systems.
• Proficiency in Python and PyTorch: Strong experience in developing and training models using PyTorch
• GPU Programming with CUDA: Hands-on experience optimizing model training and inference on GPUs using CUDA, including custom kernel development
• Distributed Computing Frameworks: Familiarity with tools like DeepSpeed, Accelerate, Unsloth, or Kubeflow for efficient large-scale model training
• Training and Fine-Tuning: Expertise in fine-tuning and quantizing transformer-based models
• Research to Production: Proven ability to translate academic research papers into scalable, production-ready code.​
• Experience in AI Research and Development: Background in AI companies or research labs, contributing to significant machine learning projects.​
• Understanding of Model Evaluation and Deployment: Experience in evaluating model performance, deploying models into production environments, and monitoring their performance post-deployment.​

Bonus Points

• Some background in hardware/electronics, gained through professional, academic, or personal projects
• Contributions to open-source initiatives
• Notable awards or publications in leading journals/conferences
• Experience thriving in a fast-paced, hyper-growth startup environment

Our Benefits

• Unlimited PTO: Recharge when you need it, no questions asked.
• Comprehensive Health Coverage: Medical, dental, and vision insurance for you and your dependents.
• Free Meals and Snacks: Daily lunches, dinners, and snacks in the office.
• Professional Growth: We invest in your continuous learning and offer opportunities to expand your skills.
• Visa Sponsorship: We welcome global talent and provide visa sponsorship to support qualified candidates.