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Senior Full Stack Machine Learning Engineer Jobs in Arizona

Bespoke Labs

Machine Learning Engineer

Flagstaff, AZ · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Bespoke Labs

Machine Learning Engineer

Phoenix, AZ · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Bespoke Labs

Machine Learning Engineer

Glendale, AZ · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Bespoke Labs

Machine Learning Engineer

Scottsdale, AZ · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Trajan Wealth LLC

Sr. Full Stack Engineer

Scottsdale, AZ · On-site

$108K - $140K/yr

Role Overview We are seeking a Senior Full Stack Engineer with deep API management experience to ... learning.Blameless incident culture, with the expectation that postmortems lead to real change.

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Senior Full Stack Machine Learning Engineer information

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

AspectSenior Full Stack Machine Learning EngineerData Scientist
CredentialsBachelor's/Master's in CS, Data Science, or related; experience with ML frameworksBachelor's/Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops end-to-end ML applications, integrates backend and frontendAnalyzes data, builds models, visualizes insights
Industry UsageTech, finance, healthcare, where deploying ML models is essentialResearch, analytics, consulting across various sectors

While both roles involve working with data and machine learning, the Senior Full Stack Machine Learning Engineer focuses on building and deploying complete ML solutions, including frontend and backend integration. In contrast, Data Scientists primarily analyze data and develop models to generate insights. The engineer's role is more application-oriented, whereas the Data Scientist's role is more research and analysis-focused.

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

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

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Cities in Arizona with the most Senior Full Stack Machine Learning Engineer job openings:

Senior Full Stack Machine Learning Engineer jobs near you

Machine Learning Engineer

Bespoke Labs

Flagstaff, AZ • On-site

Full-time

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

Job description

About Us

We are AI researchers and builders who understand how to curate data and RL environments that truly improve models. We curated OpenThoughts, one of the best open reasoning datasets, and have trained SOTA models such as Bespoke-MiniCheck and Bespoke-MiniChart.

We are embarked on a journey to build Environments that are entire digital worlds that can be used to push the frontier of agents.

What You'll Be Working On

You will work directly with our research team on RL environment and task creation for agent training. This means designing observation spaces, action spaces, reward signals, and success criteria for new environments — and building the infrastructure that makes world-scale RL training possible. This is a high-ownership role; you will be building novel systems, not maintaining legacy ones.

Must-Have Skills

3+ years of ML engineering experience — model training, fine-tuning, or post-training pipelines in research or production

Strong Python and deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed precision)

Hands-on experience with LLM post-training — SFT, RLHF, PPO, DPO, or reward model training — and understanding of how training data quality affects model behavior

Familiarity with RL frameworks (Gymnasium, dm_env) and the ability to design or modify reward functions for agent training objectives

Experience running experiments at scale on cloud or HPC (AWS, GCP, SLURM, or Ray)

Solid understanding of evaluation methodology — held-out sets, benchmark design, avoiding train/eval contamination

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