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Trainee Machine Learning Engineer Jobs in Arizona

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 ...

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

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Trainee Machine Learning Engineer information

What is the difference between Trainee Machine Learning Engineer vs Junior Data Scientist?

AspectTrainee Machine Learning EngineerJunior Data Scientist
Required CredentialsBasic programming, introductory ML knowledge, possibly a degree in CS or related fieldDegree in Data Science, Statistics, or related field; some programming experience
Work EnvironmentInternship or entry-level role in tech or AI companies, labs, or startupsEntry-level position in data teams across various industries
Employer & Industry UsageTech companies, AI startups, research labsFinance, healthcare, e-commerce, and tech firms

While both roles are entry-level and involve working with data, a Trainee Machine Learning Engineer focuses more on developing and deploying machine learning models, whereas a Junior Data Scientist emphasizes data analysis, visualization, and insights. The roles often overlap, but the Trainee ML Engineer is more specialized in ML algorithms and model deployment.

What are the most commonly searched types of Machine Learning Engineer jobs in Arizona?

The most popular types of Machine Learning Engineer jobs in Arizona are:

What are popular job titles related to Trainee Machine Learning Engineer jobs in Arizona?

For Trainee Machine Learning Engineer jobs in Arizona, the most frequently searched job titles are:

What job categories do people searching Trainee Machine Learning Engineer jobs in Arizona look for?

The top searched job categories for Trainee Machine Learning Engineer jobs in Arizona are:

What cities in Arizona are hiring for Trainee Machine Learning Engineer jobs?

Cities in Arizona with the most Trainee Machine Learning Engineer job openings:

Infographic showing various Trainee Machine Learning Engineer job openings in Arizona as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 24% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Machine Learning Engineer

Prosum Inc.

Phoenix, AZ • On-site

$130K - $150K/yr

Other

Posted 26 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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