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Machine Learning Hardware Jobs (NOW HIRING)

Jobtailor

Machine Learning Engineer

California, MO · On-site

$130 - $190/hr

... optimize hardware utilization for data collection * automate synthetic datasets creation ... Machine Learning Engineering * Data Pipeline * Data Processing * Synthetic Data Creation * Real ...

Avride

Machine Learning Engineer

Austin, TX · On-site

The role involves developing and optimizing machine learning models, managing large-scale datasets ... Optimize inference performance, model compression, and deployment across various hardware platforms ...

Neuralink

Machine Learning Engineer

Austin, TX · On-site

$199K - $331K/yr

Neuralink designs all hardware in-house, from custom ASICs to thin-film arrays. There is no part of the technical design that cannot change. Learnings from your work will directly influence next ...

Spear AI

Machine Learning Engineer

They are seeking a skilled Machine Learning Engineer to build and deploy production ML systems for ... Spear AI develops AI-powered software and hardware for maritime operations. Founded in 2021, the ...

Tacit

Machine Learning Scientist

San Francisco, CA · On-site

$180K - $270K/yr

About the role As a Machine Learning Scientist , you will develop cutting-edge AI models to ... Iterate rapidly on model prototypes for real-time inference on custom hardware. * Create and ...

Tacit

Machine Learning Scientist

San Francisco, CA · On-site

$180 - $270/hr

As a Machine Learning Scientist, you will develop cutting‑edge AI models to integrate and decode ... Iterate rapidly on model prototypes for real‑time inference on custom hardware. * Create and ...

Ayosemi

Machine Learning Architect

Boston, MA · On-site

$120 - $160/hr

Work with hardware engineers to define and refine processor architecture based on insights learned through model training and experimentation. * Maintain a deep curiosity about what makes machine ...

New

Neuralink

Machine Learning Engineer

Austin, TX

Neuralink designs all hardware in-house, from custom ASICs to thin-film arrays. There is no part of the technical design that cannot change. Learnings from your work will directly influence next ...

elobau GmbH

## Werkstudent AI/Machine Learning (m/w/d)Applylocations: Leutkirch Werk 1time type: Part timeposted ... Du erhältst von uns moderne Hardware für die Erstellung Deiner Arbeit.* Du bekommst ein ...

New

Velo3D

Machine Learning Engineer

Fremont, CA · On-site

$150K - $220K/yr

We are seeking a Machine Learning Engineer to join our team developing machine learning solutions ... The VELO3D award-winning solution includes an integrated offering of hardware and software:

Velo3D

Machine Learning Engineer

Fremont, CA

$150K - $220K/yr

We are seeking a Machine Learning Engineer to join our team developing machine learning solutions ... The VELO3D award-winning solution includes an integrated offering of hardware and software:

Velo3D

Machine Learning Engineer

Fremont, CA · On-site

$150K - $220K/yr

We are seeking a Machine Learning Engineer to join our team developing machine learning solutions ... The VELO3D award-winning solution includes an integrated offering of hardware and software:

MatrixSpace

Machine Learning Engineer

Burlington, MA · Remote

$165K - $200K/yr

Experience with embedded systems, GPUs, NPUs, FPGAs, or hardware acceleration. * Familiarity withMLOps, CI/CD, model monitoring, and large-scale production systems. At MatrixSpace, Machine Learning ...

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How much do machine learning hardware jobs pay per hour?

As of Aug 21, 2026, the average hourly pay for machine learning hardware in the United States is $24.59, according to ZipRecruiter salary data. Most workers in this role earn between $17.55 and $27.88 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.

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Machine Learning Hardware jobs near you
Infographic showing various Machine Learning Hardware job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 23% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $51,154 per year, or $24.6 per hour.

Machine Learning Engineer

Jobtailor

California, MO • On-site

$130 - $190/hr

Other

Posted 16 days ago

Job description

Responsibilities
  • build dynamic troubleshooting agents that understand networks
  • solve unstructured production log data complexities
  • optimize hardware utilization for data collection
  • automate synthetic datasets creation
  • architect data infrastructure for real-time network failures analysis
  • design and scale automated pipelines transforming raw production logs into insights
  • develop systems generating synthetic data for edge cases learning
  • tackle unique network complexity problems
  • optimize data collection and hardware utilization
Requirements
  • Bachelor's degree in STEM and 5+ years of relevant experience
  • Master's degree in STEM and 3+ years of relevant experience
  • PhD in STEM +0 years of relevant experience or equivalent related work experience
  • 5+ years of experience in data engineering, machine learning engineering, or related roles
  • Data Pipeline experience, designing and scaling data pipelines for unstructured or semi-structured data, including ingestion, cleansing, and auditing
  • ML Infrastructure experience working with ML data workflows, including dataset creation, labeling, and evaluation
  • Experience with Python and data processing frameworks (e.g., Spark, Beam, Ray)
  • Experience with ML systems and tools, such as training pipelines and model evaluation frameworks
  • Experience with human-in-the-loop ML systems, active learning, weak supervision or self-evolving agents (preferred)
  • Exposure large language models, computer vision, or speech datasets (preferred)
  • Experience building internal tools or platforms used by annotation or operations teams (preferred)
Hard Skills
  • Data Engineering
  • Machine Learning Engineering
  • Data Pipeline
  • Data Processing
  • Synthetic Data Creation
  • Real-Time Analysis
  • Network Troubleshooting
  • Data Cleansing
  • Model Evaluation
  • Active Learning
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