Machine Learning Systems Engineer
Voltai Com
Apply to this job Menlo Park, CA on site Until 8/21/2026 First posted May 25, 2026 Last posted May 25, 2026
Job description
About Voltai
Voltai 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 Team
Our 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
- 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
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.
About this role
Summary
Design optimize ml pipelines for training, inference of large language models on hardware
Job title
Machine Learning Systems Engineer
Experience level
not specified
Industry
semiconductor and electronics
Location requirements
Menlo Park, CA; remote work not specified, likely on-site
Salary
Not specified
Management role
No
Skills & keywords
Required skills
cc++rustcudaquantizationawqgptqggufggml inference enginessystem performance
Preferred skills
hardwareelectronicsopen-sourcepublicationsfast-paced environment
Specializations
ml pipelinestransformer operationscuda kernelslow-precision computationdistributed training
Locations
Structured locations inferred from the posting.
Menlo Park, CA, USA
On-site City
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