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# Machine Learning Systems Engineer

- **Company:** Voltai Inc.
- **Location:** Menlo Park, CA, United States
- **Contract:** Permanent contract
- **Skills:** Systems Engineering, C++ (Programming Language), Nvidia CUDA, Computer Engineering, Distributed Computing Environment, Monitoring of Systems, High-Level Architecture, Machine Learning, Open Source Technology, Performance Tuning, Graphics Processing Unit (GPU), Large Language Models, GGUF, Optimization Algorithms, SGLang, Reasoning Engines, Machine Learning Operations, TensorRT, VLLM
- **Published:** November 2, 2026
- **Apply:** https://www.careerbuilder.com/job-details/machine-learning-systems-engineer-menlo-park-ca--2b4083a7-2622-4338-8c88-3483a01f710a

## About the Role

Machine Learning, Systems Engineering, Electronics, Semiconductor Manufacturing, Printed Circuit Board Design, Consumer Electronics, Computer Engineering, Internet of Things, Semiconductors, Electronic Design

## Description

* Design and maintain high-performance ML pipelines for training, evaluation, and inference of LLMs and retrieval-augmented systems, with a focus on hardware efficiency and throughput
 * Optimize core transformer operations at the kernel level, designing and tuning custom kernels and low-level implementations for GPU-accelerated workloads
 * Implement and integrate low-precision computation techniques to reduce memory footprint and accelerate inference with minimal accuracy degradation
 * Build and maintain inference engines for on premises deployments
 * Architect distributed training and inference systems
 * Collaborate closely with researchers and infra teams to bring cutting-edge model innovations into production
 * Interface directly with enterprise hardware environments, tuning performance based on real-world deployment constraints

Required Skill Sets

 * Languages: Expertise in C, C++, or Rust
 * Design and Optimize CUDA Kernels for LLMs: Develop and fine-tune custom CUDA kernels to accelerate core transformer operations
 * Implement Low-Precision Computation Techniques: Apply quantization methods like AWQ and GPTQ to reduce model size and inference latency. Ensure minimal accuracy loss while maximizing throughput on GPU architectures with familiarity with concepts like GGUF and GGML
 * Develop and Maintain High-Performance Inference Systems: Build, improve, and maintain inference engines such as vLLM, SGLang, and TensorRT with a focus on low-latency and high throughput
 * Architect Distributed Training and Inference Solutions: Design systems that support model parallelism (tensor, pipeline, expert etc) to enable efficient training and inference across multiple GPUs and nodes
 * Integrate Research into Production Systems: Translate cutting-edge research findings into robust, production-ready systems. Ensure that innovations in model architectures and optimization techniques are effectively deployed.
 * Monitor and Optimize System Performance: Implement monitoring tools to track system metrics, identify bottlenecks, and optimize performance

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