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Executive Full Stack Machine Learning Engineer Jobs in Cupertino, CA

Winaxis

Machine Learning Engineer

Fremont, CA · On-site

Machine Learning Engineer Location: Fremont, CA (Local) Onsite interview Duration: 12+ Mos H1B Only h1 candidate About the Role: Our direct client is hiring a Machine Learning Engineer for their ...

Pattern AI

Machine Learning Engineer

San Mateo, CA · On-site +1

Company Description PatternAI is an automated machine learning platform that reveals critical patterns in data for narrow business problems. We're seeking an outstanding ML Engineer to join our data ...

Apex Systems

Machine Learning Engineer

Pleasanton, CA · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

Manage the full lifecycle of machine learning models in a production setting. * Leverage MLOps ... Programming languages: Python, R, and SQL * Machine learning frameworks such as TensorFlow, Keras ...

Pattern AI

Machine Learning Engineer

San Mateo, CA

Company Description PatternAI is an automated machine learning platform that reveals critical patterns in data for narrow business problems. We're seeking an outstanding ML Engineer to join our data ...

MM International

Machine Learning Engineer

Fremont, CA · On-site

Machine Learning Engineer Location: Fremont, CA once the documents are verified, a Codility assessment will be shared with the candidate, where they need to score a minimum of 70% and post that, a ...

Apple

Machine Learning Engineer

Sunnyvale, CA · On-site

Description Apple's Video Computer Vision (VCV) Face and Body technologies team is looking for a skilled Machine Learning Engineer with experience developing ML models for computer vision and ...

Apple

Machine Learning Engineer

Sunnyvale, CA · On-site

Description Apple's Video Computer Vision (VCV) Face and Body technologies team is looking for a skilled Machine Learning Engineer with experience developing ML models for computer vision and ...

Gotion, Inc.

Machine Learning Engineer

Fremont, CA

We lead the full product development cycle, integrating mechanical, electrical, thermal, and ... Role Summary We are seeking a highly motivated Machine Learning Engineer with a strong background ...

Saige Partners

Full Stack Developer

San Jose, CA

$80 - $90/hr

Full Stack Developer (San Jose, CA) Job Overview: We are seeking a Full Stack Software Developer ... Solid knowledge of PostgreSQL and NoSQL databases Familiarity with machine learning algorithms ...

Protingent

Full Stack Software Developer

San Jose, CA · On-site

$80 - $90/hr

  • Medical

  • Retirement

  • PTO

Job Title : Full Stack Software Developer Position Description : Protingent Staffing has an ... machine learning algorithms (supervised/unsupervised). • Experience with AI/ML frameworks ...

Skild AI

Machine Learning Engineer

San Mateo, CA

Position Overview We are looking for a Machine Learning Engineer to be responsible for designing and implementing cutting-edge reinforcement learning algorithms, conducting experiments, and ...

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All JobsFull Stack Machine Learning Engineer JobsExecutive Full Stack Machine Learning Engineer JobsExecutive Full Stack Machine Learning Engineer Jobs in Cupertino, CA

Executive Full Stack Machine Learning Engineer information

See Cupertino, CA salary details

$54.9K

$166.3K

$235K

How much do executive full stack machine learning engineer jobs pay per year?

As of Aug 18, 2026, the average yearly pay for executive full stack machine learning engineer in Cupertino, CA is $166,272.00, according to ZipRecruiter salary data. Most workers in this role earn between $136,900.00 and $194,900.00 per year, depending on experience, location, and employer.

What is the difference between Executive Full Stack Machine Learning Engineer vs Data Scientist?

AspectExecutive Full Stack Machine Learning EngineerData Scientist
CredentialsBachelor's/Master's in CS, Engineering, or related; often requires experience in ML and full stack developmentBachelor's/Master's in Data Science, Statistics, or related; strong analytical and statistical skills
Work EnvironmentDevelops end-to-end ML solutions, integrates backend and frontend, collaborates with engineering teamsAnalyzes data, builds models, visualizes insights, often in research or analytics teams
Industry UsageUsed in tech companies, startups, and enterprises deploying ML productsCommon in research institutions, analytics firms, and data-driven organizations

The Executive Full Stack Machine Learning Engineer focuses on building and deploying complete ML solutions, combining software engineering and data science skills. In contrast, Data Scientists primarily analyze data and develop models without necessarily handling full stack development. Both roles require strong technical credentials but differ in scope and daily tasks.

What are popular job titles related to Executive Full Stack Machine Learning Engineer jobs in Cupertino, CA?

For Executive Full Stack Machine Learning Engineer jobs in Cupertino, CA, the most frequently searched job titles are:

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Executive Full Stack Machine Learning Engineer jobs near you
Infographic showing various Executive Full Stack Machine Learning Engineer job openings in Cupertino, CA as of August 2026, with employment types broken down into 1% As Needed, 72% Full Time, 26% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $166,272 per year, or $79.9 per hour.

(6300-1009) Software Engineer Machine Learning Software Stack

Achronix Semiconductor Corporation

Santa Clara, CA • On-site

Full-time

This job post has expired 1 day ago. Applications are no longer accepted.

Job description

Job Description/Responsibilities
At Achronix Semiconductor we develop FPGA-based hardware, software, and systems solutions to accelerate critical applications in areas such as 5G wireless infrastructure, network switching, and data center services. We work in small highly motivated teams of domain experts across the full product range, from high-level systems software to digital and analog circuit design, to create innovative products that are first-to-market and solve critical business needs.
You will have a unique opportunity to collaborate on a small team driving the development of a complete software and hardware stack for an FPGA-based machine learning inference accelerator card reference platform. You will adapt existing open-source and university software when possible, and develop new software from scratch as needed, to assemble a complete full-stack end-to-end software solution. This role works closely with sales and marketing, systems engineers, EDA tool developers, and FPGA architects. The resulting system will support diverse internal and external use models including FPGA fabric optimization, accelerator micro-architecture exploration, memory subsystem design, place & route software verification, system prototyping, pre-sales demonstrations, and customer deployment and scaling.
Prior experience is required working with an existing open-source or proprietary machine learning accelerator architecture such as OpenTPU, NVDLA, Eyeriss, or VTA. You must have a background in compiler hacking on one or more of TVM, Glow, Halide, Spatial, XLA, CLANG, LLVM, or GCC. Experience desired with compiler Intermediate Representations (IRs) and back-ends; JIT compilers; kernel-mode and user-mode Windows, Unix, or embedded systems runtime environments and device drivers. Familiarity is desired with machine learning frameworks such as TensorFlow, PyTorch, Caffe2, Keras and MXNet; domain-specific languages such as Halide and Spatial; and with common DNN models such as AlexNet, ResNet50, Inception, YOLO, RNN, and LSTM.
Skills:
  • 2+ years of work or educational experience in machine leaning accelerator architectures, micro-architectures, and compilers.
  • Skilled practitioner in C++, Python, and Verilog. Familiarity with System-C, System Verilog, HLS, Catapult-C, or Chisel.
  • Experience in two of more of the following categories:
    • Machine learning accelerators such as OpenTPU, NVDLA, Eyeriss, and VTA.
    • Machine learning frameworks such as TensorFlow, PyTorch, Caffe2, Keras, and MXNet.
    • Common DNN models such as AlexNet, ResNet50, Inception, YOLO, RNN, and LSTM.
    • Domain-specific languages such as Halide and Spatial.
    • Compilers such as TVM, Glow, Halide, CLANG, LLVM, and GCC.
    • Embedded system runtime environments and device drivers

Education:
MS or PhD in Computer Science, Computer Engineering, Electrical Engineering, Applied Math, or Physics

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