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# Machine Learning Engineer

- **Company:** Voltai Inc.
- **Location:** Menlo Park, CA, United States
- **Contract:** Permanent contract
- **Skills:** Clean Code Principles, Artificial Intelligence, Code Generation, Nvidia CUDA, Computer Programming, Computer Engineering, Distributed Computing Environment, Python (Programming Language), Search Algorithms, Linux Kernel, Machine Learning, Open Source Technology, Performance Tuning, Graphics Processing Unit (GPU), Pytorch, DeepSpeed, Evaluation Pipelines, Large Language Models, Model Validation, Gpu Programming, Kubernetes, Production Code, Machine Learning Operations
- **Published:** October 27, 2026
- **Apply:** https://www.careerbuilder.com/job-details/machine-learning-engineer-menlo-park-ca--175a34f0-525b-4044-8903-d3576770f998

## About the Role

Machine Learning, Electronics, Semiconductor Manufacturing, Printed Circuit Board Design, Consumer Electronics, Computer Engineering, Internet of Things, Semiconductors, Electronic Design, Artificial Intelligence (AI), * 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

## Description

* 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