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Machine Learning Hardware Jobs in Sunnyvale, CA (NOW HIRING)

Human Archive

Machine Learning Engineer

San Francisco, CA · On-site

$100K - $150K/yr

To achieve this, we build custom hardware products, deploy them globally at scale, and publish ... The Opportunity As a Machine Learning Engineer, you'll work on multimodal perception, VLA training ...

Voltai

Machine Learning Engineer

Menlo Park, CA · On-site

The Machine Learning Engineer will architect and develop high-performance AI systems, manage large ... Preferred : • Some background in hardware/electronics, gained through professional, academic, or ...

Apple

Machine Learning Video Engineer

Cupertino, CA · On-site

... and other specialized hardware components to develop robust, viable solutions. Minimum ... in machine learning Knowledge of Video or Image processing or Computer Vision Solid programming ...

Meta

Software Engineer, Machine Learning

Sunnyvale, CA

$347K/yr

Software Engineer, Machine Learning Responsibilities: * Define and own the technical architecture ... hardware utilization, and leading cross-org efforts to resolve them * Influence the broader ML ...

Meta

Software Engineer, Machine Learning

Menlo Park, CA · On-site

$347K/yr

Software Engineer, Machine Learning Responsibilities: * Define and own the technical architecture ... hardware utilization, and leading cross-org efforts to resolve them * Influence the broader ML ...

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How much do machine learning hardware jobs pay per hour?

As of Sep 2, 2026, the average hourly pay for machine learning hardware in Sunnyvale, CA is $28.86, according to ZipRecruiter salary data. Most workers in this role earn between $20.58 and $32.74 per hour, depending on experience, location, and employer.

What is a machine learning hardware?

A Machine Learning Hardware job involves designing, optimizing, and developing specialized hardware to accelerate machine learning workloads. Professionals in this field work on hardware architectures like GPUs, TPUs, FPGAs, and custom accelerators to improve efficiency, performance, and power consumption. They collaborate with software engineers and data scientists to optimize hardware-software co-design. This role requires expertise in computer architecture, parallel computing, and low-level programming.

What are the typical day-to-day responsibilities for a machine learning hardware engineer?

As a Machine Learning Hardware engineer, your daily tasks often include collaborating with data scientists and software engineers to understand computational requirements, designing and prototyping hardware accelerators, and optimizing existing architectures for improved performance and efficiency. You might work with simulation tools to model new designs, validate hardware functionality, and troubleshoot issues during integration. The role typically involves both independent technical work and teamwork across hardware and AI/ML departments. This position requires keeping up to date with emerging technologies to ensure your solutions remain cutting-edge and competitive in the fast-evolving landscape of artificial intelligence.

What are the key skills and qualifications needed to thrive in the machine learning hardware position, and why are they important?

To thrive in Machine Learning Hardware, you need a solid background in computer engineering, digital design, and machine learning principles, often supported by a degree in electrical engineering, computer engineering, or a related field. Familiarity with hardware description languages (such as VHDL or Verilog), simulation tools, FPGA/ASIC development platforms, and possibly certifications in hardware design or ML accelerators is valuable. Collaboration, problem-solving, and the ability to communicate complex technical ideas effectively are essential soft skills. These skills enable you to design and optimize specialized hardware solutions that accelerate machine learning workloads and foster interdepartmental innovation.

What are the most commonly searched types of Machine Learning Hardware jobs in Sunnyvale, CA?

The most popular types of Machine Learning Hardware jobs in Sunnyvale, CA are:

What are popular job titles related to Machine Learning Hardware jobs in Sunnyvale, CA?

For Machine Learning Hardware jobs in Sunnyvale, CA, the most frequently searched job titles are:

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The top searched job categories for Machine Learning Hardware jobs in Sunnyvale, CA are:

Machine Learning Hardware jobs near you
Infographic showing various Machine Learning Hardware job openings in Sunnyvale, CA as of August 2026, with employment types broken down into 1% As Needed, 72% Full Time, 25% Part Time, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $60,037 per year, or $28.9 per hour.

Machine Learning Engineer

Human Archive

San Francisco, CA • On-site

$100K - $150K/yr

Full-time

Re-posted 12 days ago

Job description

About Human Archive
Human Archive is a research lab focused on modeling human embodied intelligence.
Humans are the most sophisticated biological systems we have ever observed, yet we still do not fully understand ourselves. Research into human physical intelligence - including the human hand, proprioception, and vision - remains largely unsolved. Our mission is to recover human embodied intelligence as a learned model. To achieve this, we build custom hardware products, deploy them globally at scale, and publish research. Today, our data is used for robotics and world modeling, but the broader opportunity is advancing scientific research into intelligence itself.
Founded by Stanford and UC Berkeley researchers, we are lean, deeply technical, and operate at extreme speed, taking on unglamorous and conventionally impossible problems that directly unlock step-function gains in model capability.
The deployment of capable humanoids at scale will permanently redefine human labor. Undesirable physical work will disappear, and human effort will shift toward a new era of abundant creativity.
We are building the infrastructure to accelerate that transition by assembling the Human Archive mafia. You will own meaningful systems from day one and see your work directly impact model capabilities. This is a once-in-a-generation inflection point. If you want to help reshape physical labor and work on problems that matter at civilizational scale, join us.
The Opportunity
As a Machine Learning Engineer, you'll work on multimodal perception, VLA training, robotics post-training, and downstream policy evaluation. This is a hands-on role at the intersection of applied machine learning, data infrastructure, and robotics, where your work directly shapes how data is collected, validated, annotated, and evaluated.
You'll help close the loop between research and data collection by fine-tuning VLAs on downstream policy performance and building post-training and reinforcement learning systems around real-world robotics tasks. You'll be expected to make architectural decisions, own projects end-to-end, and operate in highly ambiguous research environments given the novelty and scale of our multimodal datasets.
Your work will help shape how frontier labs and leading robotics companies train their models, transforming physical labor markets and economies while contributing to broader research into human embodied intelligence.
What You'll Do
  • Build systems for multimodal perception, annotation, dataset QA, and robotics evaluation
  • Publish research on multimodal data by fine-tuning and evaluating VLA models on downstream robotics tasks and policy performance
  • Build post-training and reinforcement learning systems around robotics failure modes and corrective demonstrations
  • Work across video understanding, tracking, pose estimation, temporal modeling, and multimodal alignment
  • Develop tooling for benchmarking, observability, and temporal efficiency
  • Prototype quickly, ship rapidly, and iterate from real-world robotics deployments and research feedback
What We're Looking For
  • Passionate, mission-driven individuals who have demonstrated exceptional ownership in previous work
  • Engineers who want their work to directly impact the next frontier of physical AGI
  • Strong ML engineering fundamentals across robotics, computer vision, and perception systems
  • Experience with video understanding, tracking, pose estimation, robotics, or real-world sensor systems
  • Strong technical intuition and ability to move quickly in ambiguous research environments
  • Published research or production experience in robotics, embodied AI, reinforcement learning, motion capture, or vision systems is a strong plus

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