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Executive Full Stack Machine Learning Engineer Jobs in Scottsdale, AZ

Prosum Inc.

Machine Learning Engineer

Phoenix, AZ · On-site

$130K - $150K/yr

Sr. Machine Learning Engineer Salary Range: $130k to $150k Our client is seeking a Sr. Machine Learning Engineering for a direct hire role to sit in North Phoenix, AZ or Hillsboro, OR. This role will ...

Bespoke Labs

Machine Learning Engineer

Phoenix, AZ · On-site

Integrate RL environments into the training stack, working with environment authors on interfaces, reward plumbing, and agent loop mechanics * Implement methods from recent ML papers quickly and turn ...

Bespoke Labs

Machine Learning Engineer

Chandler, AZ · On-site

Integrate RL environments into the training stack, working with environment authors on interfaces, reward plumbing, and agent loop mechanics * Implement methods from recent ML papers quickly and turn ...

Bespoke Labs

Machine Learning Engineer

Chandler, AZ

Integrate RL environments into the training stack, working with environment authors on interfaces, reward plumbing, and agent loop mechanics * Implement methods from recent ML papers quickly and turn ...

Bespoke Labs

Machine Learning Engineer

Glendale, AZ

Integrate RL environments into the training stack, working with environment authors on interfaces, reward plumbing, and agent loop mechanics * Implement methods from recent ML papers quickly and turn ...

Bespoke Labs

Machine Learning Engineer

Phoenix, AZ

Integrate RL environments into the training stack, working with environment authors on interfaces, reward plumbing, and agent loop mechanics * Implement methods from recent ML papers quickly and turn ...

Bespoke Labs

Machine Learning Engineer

Glendale, AZ · On-site

Integrate RL environments into the training stack, working with environment authors on interfaces, reward plumbing, and agent loop mechanics * Implement methods from recent ML papers quickly and turn ...

Bespoke Labs

Machine Learning Engineer

Mesa, AZ · On-site

Integrate RL environments into the training stack, working with environment authors on interfaces, reward plumbing, and agent loop mechanics * Implement methods from recent ML papers quickly and turn ...

Bespoke Labs

Machine Learning Engineer

Mesa, AZ

Integrate RL environments into the training stack, working with environment authors on interfaces, reward plumbing, and agent loop mechanics * Implement methods from recent ML papers quickly and turn ...

Bespoke Labs

Machine Learning Engineer

Scottsdale, AZ · On-site

Integrate RL environments into the training stack, working with environment authors on interfaces, reward plumbing, and agent loop mechanics * Implement methods from recent ML papers quickly and turn ...

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All JobsFull Stack Machine Learning Engineer JobsExecutive Full Stack Machine Learning Engineer JobsExecutive Full Stack Machine Learning Engineer Jobs in Scottsdale, AZ

Executive Full Stack Machine Learning Engineer information

See Scottsdale, AZ salary details

$44.8K

$135.8K

$191.9K

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

As of Sep 4, 2026, the average yearly pay for executive full stack machine learning engineer in Scottsdale, AZ is $135,779.00, according to ZipRecruiter salary data. Most workers in this role earn between $111,800.00 and $159,200.00 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 Scottsdale, AZ?

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

What job categories do people searching Executive Full Stack Machine Learning Engineer jobs in Scottsdale, AZ look for?

The top searched job categories for Executive Full Stack Machine Learning Engineer jobs in Scottsdale, AZ are:

What cities near Scottsdale, AZ are hiring for Executive Full Stack Machine Learning Engineer jobs?

Cities near Scottsdale, AZ with the most Executive Full Stack Machine Learning Engineer job openings:

Executive Full Stack Machine Learning Engineer jobs near you
Infographic showing various Executive Full Stack Machine Learning Engineer job openings in Scottsdale, AZ as of June 2026, with employment types broken down into 96% Full Time, 1% Part Time, 2% Temporary, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $135,779 per year, or $65.3 per hour.

Machine Learning Engineer

Prosum Inc.

Phoenix, AZ • On-site

$130K - $150K/yr

Other

Posted 28 days ago

Job description

Job Description
Sr. Machine Learning Engineer
Salary Range: $130k to $150k
Our client is seeking a Sr. Machine Learning Engineering for a direct hire role to sit in North Phoenix, AZ or Hillsboro, OR. This role will be onsite 4 days a week and 1 remote day.
JOB SUMMARY
The role of Senior Machine Learning Engineer will architect and optimize real-time, high-throughput, and ultra-low latency image pipelines for next-generation Mask Inspection Tools. Responsibilities include eliminating hardware bottlenecks through CUDA kernel tuning and GPU parallel computing, ensuring deep learning models and CV algorithms seamlessly processing massive, high-bandwidth streaming data at production scale.
ESSENTIAL DUTIES AND RESPONSIBILITIES
High-Performance Computing Pipeline Architecture
  • Design, implement, and optimize high-throughput, low-latency image processing pipelines for real-time optical inspection and machine vision systems.
  • Develop scalable architectures capable of processing large volumes of imaging data while meeting stringent latency and reliability requirements.
  • Profile and optimize system performance across CPU, GPU, memory, and I/O subsystems.
GPU Acceleration
  • Design, develop, and optimize CUDA kernels to accelerate deep learning inference and classical computer vision algorithms.
  • Maximize GPU utilization through efficient memory management, kernel optimization, and parallel programming techniques.
  • Evaluate and implement performance improvements using NVIDIA GPU technologies and profiling tools.
Model Deployment & Optimization
  • Optimize, quantize, and deploy machine learning models using TensorRT, ONNX Runtime, or similar inference frameworks.
  • Integrate AI models into production-grade C++ and Python applications.
  • Improve inference throughput, latency, and resource utilization while maintaining model accuracy.
  • Develop automated deployment and validation pipelines for machine learning models.
Concurrency & Systems Optimization
  • Architect and implement multi-threaded, high-concurrency software components for data acquisition, buffering, streaming, and real-time processing.
  • Design robust synchronization and communication mechanisms between hardware interfaces and AI processing pipelines.
  • Optimize end-to-end system performance for deterministic, real-time execution.
Cross-Functional Collaboration
  • Partner with machine learning scientists, computer vision engineers, hardware engineers, and software developers to deliver integrated AI solutions.

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