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Pytorch Developer Jobs in Arizona (NOW HIRING)

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 ...

OmegaHires

Java Ai Architect

Phoenix, AZ · On-site

$62.50 - $84.50/hr

Experience with RAG, Prompt Engineering, AI Agents, and Vector Databases (Pinecone, Milvus ... Knowledge of Machine Learning frameworks such as TensorFlow or PyTorch.

MY DR NOW

AI & Machine Learning Engineer

Chandler, AZ · On-site

$100K - $110K/yr

TensorFlow, PyTorch, Scikit-learn, MLflow * Epic Clarity, Epic Caboodle, FHIR, HL7 * Claude Code, GitHub Copilot, Cursor * Git, Azure DevOps, CI/CD, Docker, Kubernetes Company Description MY DR NOW ...

Tata Consultancy Service Limited

AI Engineer

Phoenix, AZ · On-site

$100K - $120K/yr

... PyTorch, TensorFlow, Hugging Face. • Practical knowledge of model orchestration frameworks (e.g., LangChain, LlamaIndex, CrewAI), Familiarity with vector databases • Experience with cloud ...

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Pytorch Developer information

What is a PyTorch developer?

A PyTorch Developer is a software engineer or data scientist who specializes in using PyTorch, an open-source machine learning library, to build and deploy deep learning models. Their responsibilities typically include designing neural network architectures, training and evaluating models, and optimizing code for performance. PyTorch Developers work in fields such as artificial intelligence, computer vision, and natural language processing, collaborating with teams to solve complex problems using machine learning. They are proficient in Python and have a strong understanding of deep learning concepts. Additionally, they often contribute to research, development, and the deployment of AI solutions in production environments.

What are the key skills and qualifications needed to thrive as a PyTorch developer, and why are they important?

To thrive as a Pytorch Developer, you need strong programming skills in Python, a solid grasp of machine learning concepts, and experience with deep learning frameworks—especially PyTorch itself. Familiarity with tools like CUDA, Jupyter Notebooks, and version control systems (e.g., Git) is typically expected, along with knowledge of cloud platforms or relevant certifications. Problem-solving ability, effective collaboration, and clear communication are crucial soft skills for success in this role. These skills and qualities are vital for efficiently building, optimizing, and deploying machine learning models in real-world applications.

What is the difference between Pytorch Developer vs Machine Learning Engineer?

AspectPytorch DeveloperMachine Learning Engineer
Required CredentialsBachelor's or higher in CS, experience with PyTorchBachelor's or higher in CS, data science, or related field, with ML experience
Work EnvironmentResearch labs, AI startups, tech companies focusing on deep learningTech companies, finance, healthcare, often involving deployment and scaling ML models
Industry UsagePrimarily in AI research and development teamsAcross industries implementing ML solutions in production

While both roles require knowledge of machine learning and experience with PyTorch, a Pytorch Developer mainly focuses on developing and optimizing deep learning models using PyTorch. A Machine Learning Engineer often has a broader scope, including deploying, maintaining, and scaling ML models across various platforms and industries.

What are some common challenges PyTorch developers face when deploying machine learning models to production environments?

Pytorch Developers often encounter challenges when transitioning models from research to production, such as optimizing model performance for inference speed and memory usage, ensuring compatibility with deployment frameworks like TorchScript or ONNX, and managing dependencies across different systems. Additionally, integrating PyTorch models into existing software stacks and maintaining reproducibility can be complex. Collaborating closely with DevOps and data engineering teams is crucial to address these issues and ensure smooth deployment.
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What job categories do people searching Pytorch Developer jobs in Arizona look for? The top searched job categories for Pytorch Developer jobs in Arizona are:
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Pytorch Developer jobs near you
Infographic showing various Pytorch Developer job openings in Arizona as of August 2026, with employment types broken down into 79% Full Time, 3% Part Time, and 18% Contract. Highlights an 81% Physical, 4% Hybrid, and 15% Remote job distribution.

Machine Learning Engineer

Bespoke Labs

Glendale, AZ • On-site

Full-time

Re-posted 20 days ago

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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