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Freelance Machine Learning Engineer Jobs in Tempe, AZ

* 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 ...

* 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 ...

AI & Machine Learning Engineer

Chandler, AZ · On-site

$100K - $110K/yr (+ commission)

Knowledge of Machine Learning and Generative AI frameworks * Strong engineering fundamentals and problem-solving skills * A builder mindset -- curious, resourceful, fast-moving, and focused on ...

Showing results 21-40

Freelance Machine Learning Engineer information

See Tempe, AZ salary details

$14

$45

$126

How much do freelance machine learning engineer jobs pay per hour?

As of Sep 7, 2026, the average hourly pay for freelance machine learning engineer in Tempe, AZ is $45.69, according to ZipRecruiter salary data. Most workers in this role earn between $23.27 and $59.18 per hour, depending on experience, location, and employer.

What does a freelance machine learning engineer do?

A Freelance Machine Learning Engineer designs, develops, and implements machine learning models and algorithms for clients on a project basis. They work independently to analyze data, build predictive models, and help businesses solve complex problems using AI and machine learning techniques. Their responsibilities may also include data preprocessing, model evaluation, and deploying solutions into production environments. Freelance Machine Learning Engineers often collaborate remotely with teams and must manage their own schedules and client relationships.

What are the key skills and qualifications needed to thrive as a freelance machine learning engineer?

To thrive as a Freelance Machine Learning Engineer, you need expertise in programming (especially Python), a solid grasp of machine learning algorithms, and a relevant academic background such as a degree in computer science, mathematics, or engineering. Familiarity with frameworks like TensorFlow or PyTorch, cloud platforms (AWS, GCP, Azure), and experience with version control systems are typically required. Strong problem-solving, self-management, and client communication skills help set successful freelancers apart. These competencies are crucial for delivering effective solutions, managing projects independently, and building client trust in a competitive market.

How do freelance machine learning engineers typically manage client expectations and project scopes?

Freelance machine learning engineers often work with clients who may not have a deep technical understanding of AI or data science. A common challenge is clearly defining the project scope and deliverables at the outset, ensuring both parties understand what is feasible given the data, time, and budget constraints. Successful freelancers use regular progress updates, milestone-based deliverables, and transparent communication to manage expectations and avoid scope creep. Building trust through clear documentation and setting realistic timelines also helps foster long-term client relationships.

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

AspectFreelance Machine Learning EngineerData Scientist
CredentialsTypically requires a degree in computer science, data science, or related fields; certifications in machine learning or AI are a plusUsually holds a degree in statistics, data science, or related areas; certifications in data analysis or visualization are common
Work EnvironmentIndependent, project-based work often remotely for various clientsOften employed full-time in organizations or consulting roles, sometimes freelance
Industry UsageUsed across tech, finance, healthcare, and startups for deploying ML modelsApplied in research, analytics, and strategic decision-making across industries

Freelance Machine Learning Engineers focus on developing and deploying ML models independently for diverse clients, while Data Scientists analyze data to extract insights, often working within organizations. Both roles require strong technical skills, but their work scope and environment differ significantly.

What are the most commonly searched types of Machine Learning Engineer jobs in Tempe, AZ?

The most popular types of Machine Learning Engineer jobs in Tempe, AZ are:

What are popular job titles related to Freelance Machine Learning Engineer jobs in Tempe, AZ?

For Freelance Machine Learning Engineer jobs in Tempe, AZ, the most frequently searched job titles are:

What job categories do people searching Freelance Machine Learning Engineer jobs in Tempe, AZ look for?

The top searched job categories for Freelance Machine Learning Engineer jobs in Tempe, AZ are:

What cities near Tempe, AZ are hiring for Freelance Machine Learning Engineer jobs?

Cities near Tempe, AZ with the most Freelance Machine Learning Engineer job openings:

Infographic showing various Freelance Machine Learning Engineer job openings in Tempe, AZ as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 22% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $95,040 per year, or $45.7 per hour.

Machine Learning Engineer

Bespoke Labs

Phoenix, AZ

Full-time

Re-posted 21 days ago


Job description

  • 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, data curation, reward model serving, and experiment orchestration

  • Run and scale training experiments on cloud or HPC (AWS, GCP, SLURM, Ray), and debug throughput, stability, and convergence issues

  • Build evaluation harnesses and benchmark infrastructure, with held-out sets and contamination controls, so results are trustworthy

  • Read eval signal and training curves to determine whether a change actually helped, and feed findings back to the research and environment teams

  • 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 them into production-grade systems