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Ml Inference Jobs in Phoenix, AZ (NOW HIRING)

YO AI Labs

AI/ML Engineer - Remote

Phoenix, AZ · Remote

$200 - $350/hr

AI/ML Engineer Job Type: Full-Time Location: Remote Job Summary We are seeking an experienced AI/ML ... for AI training and inference. * Develop and maintain REST APIs and SDK integrations

Info Way Solutions

Performance Engineer

Phoenix, AZ · On-site

$55 - $60/hr

... inference: data/tensor/pipeline parallelism, NCCL, InfiniBand/RoCE Model compression research or MLPerf-style benchmarking experience Edge/on-device deployment (Jetson, NPUs, Core ML) if your systems ...

ApplyMint

Software Engineer I

Chandler, AZ · On-site

$100 - $125/hr

Design and implement machine learning training and batch inference pipelines on Databricks using Spark and MLflow. * Develop and deploy ML models (classification, regression, forecasting, or NLP ...

YO AI Labs

Mental Health Expert - Remote

Phoenix, AZ · Remote

$200 - $350/hr

AI/ML Engineer Job Type: Full-Time Location: Remote Job Summary We are seeking an experienced AI/ML ... for AI training and inference. * Develop and maintain REST APIs and SDK integrations

Inficare Technologies

Lead AI Engineer

Phoenix, AZ · On-site

$99K - $131K/yr

LLM infrastructure, inference, and model gateways * Evaluation, observability, and safety tooling ... LangGraph, LangChain, AirFlow, etc Agentic AI and ML * Integration of commercial and open-source ...

Cisco

Applied AI Scientist

Tempe, AZ · On-site

Explore and evaluate new AI/ML techniques, tools, and methodologies, applying relevant innovations ... and inference efficiency to minimize cost and latency while preserving accuracy. * MLOps ...

Inficare Technologies

AI Python Engineer

Phoenix, AZ · On-site

Build and enhance AI/ML and GenAI-powered solutions using Python, LLMs, RAG, prompt engineering ... Flask, FastAPI, Hugging Face, Triton Inference Server * pgVector, Milvus, Prometheus, Elasticsearch ...

Cisco

Applied AI Scientist

Phoenix, AZ · On-site

Explore and evaluate new AI/ML techniques, tools, and methodologies, applying relevant innovations ... and inference efficiency to minimize cost and latency while preserving accuracy. * MLOps ...

Deloitte

Google AI Architect

Tempe, AZ · On-site

Integrate and fine-tune Large Language Models (LLMs) and other AI/ML models into enterprise applications. Develop and implement strategies for model deployment, inference, and monitoring, with an ...

Deloitte

Google AI Architect

Gilbert, AZ · On-site

Integrate and fine-tune Large Language Models (LLMs) and other AI/ML models into enterprise applications. Develop and implement strategies for model deployment, inference, and monitoring, with an ...

Entertainment Partners

AI Engineer

Tempe, AZ · On-site

Experience serving ML models at scale using Triton Inference Server, TorchServe, Ray Serve, or similar. * Contributions to open-source ML projects or published research (papers, patents, or technical ...

Entertainment Partners

AI Engineer

Tempe, AZ · On-site

Experience serving ML models at scale using Triton Inference Server, TorchServe, Ray Serve, or similar. * Contributions to open-source ML projects or published research (papers, patents, or technical ...

Master Electronics

Data Scientist II

Phoenix, AZ · On-site

... inference techniques (uplift modeling, difference-in-differences, synthetic controls, instrumental variables) where randomized experiments aren't feasible * Translate business problems into ML ...

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All JobsMl Inference JobsMl Inference Jobs in Phoenix, AZ

Ml Inference information

See Phoenix, AZ salary details

$37.2K

$121.9K

$195.1K

How much do ml inference jobs pay per year?

As of Sep 8, 2026, the average yearly pay for ml inference in Phoenix, AZ is $121,868.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,800.00 and $135,000.00 per year, depending on experience, location, and employer.

What is ML inference?

ML inference refers to the process of using a trained machine learning model to make predictions or decisions based on new data. After a model has been trained on historical data, inference is the phase where that model is deployed and used in real-world applications, such as recognizing speech, detecting objects in images, or recommending products. The focus in ML inference is on speed, efficiency, and scalability to ensure quick predictions, often in real time. This process is critical for practical applications like mobile apps, web services, and embedded systems. Optimizing inference involves reducing latency, memory usage, and computational requirements.

What are the key skills and qualifications needed to thrive in ML inference?

To thrive in ML Inference, you need a solid background in machine learning principles, programming (Python or C++), and experience with deploying models at scale, often supported by a degree in computer science or a related field. Familiarity with frameworks and tools such as TensorFlow, PyTorch, ONNX, and cloud platforms like AWS SageMaker or Google AI Platform is typically required. Strong problem-solving skills, attention to detail, and effective communication are crucial soft skills for collaborating with multidisciplinary teams and optimizing model performance. These skills ensure efficient, scalable, and reliable deployment of machine learning solutions in real-world applications.

What are some common challenges faced by ML inference engineers when deploying models to production?

ML Inference Engineers often encounter challenges such as optimizing model latency and throughput to meet production requirements, ensuring compatibility with diverse hardware environments, and managing model versioning and updates without disrupting service. Additionally, balancing resource utilization and inference accuracy while monitoring real-time performance metrics is crucial. Collaboration with data scientists, DevOps, and software engineers is typically essential to streamline deployment and maintain robust, scalable inference pipelines.

What is the difference between Ml Inference vs Data Scientist?

AspectML InferenceData Scientist
Required CredentialsKnowledge of machine learning models, programming skillsDegree in data science, statistics, or related fields
Work EnvironmentDeploying models in production, real-time data processingData analysis, model development, research
Industry UsageAI product deployment, software companiesResearch institutions, tech firms, consulting

ML Inference focuses on deploying trained models to make predictions on new data, often in real-time. Data Scientists develop and analyze models, working primarily in research and development. While both roles require understanding of machine learning, ML Inference emphasizes deployment and operationalization, whereas Data Scientists focus on model creation and analysis.

What are popular job titles related to Ml Inference jobs in Phoenix, AZ?

For Ml Inference jobs in Phoenix, AZ, the most frequently searched job titles are:

What job categories do people searching Ml Inference jobs in Phoenix, AZ look for?

The top searched job categories for Ml Inference jobs in Phoenix, AZ are:

What cities near Phoenix, AZ are hiring for Ml Inference jobs?

Cities near Phoenix, AZ with the most Ml Inference job openings:

Ml Inference jobs near you
Infographic showing various Ml Inference job openings in Phoenix, AZ as of September 2026, with employment types broken down into 2% Internship, 76% Full Time, 4% Part Time, 3% Temporary, 10% Contract, and 5% Nights. Highlights an 77% Physical, 4% Hybrid, and 19% Remote job distribution, with an average salary of $121,868 per year, or $58.6 per hour.

IAM Engineer - Phoenix, Az

Phoenix, AZ • On-site

Motion Recruitment Partners, LLC
Recruiting and Staffing Services • 1 - 5K employees
Learn more about Motion Recruitment Partners, LLCexternal

Other

Medical, Dental, Vision, Retirement, PTO

Re-posted 7 days ago

Job description


A leading financial services firm in Chandler, AZ is seeking a Senior Machine Learning Engineer to join their technology innovation team. In this position, you'll be at the forefront of building state-of-the-art machine learning solutions, taking ownership of projects from the design phase through to on-device deployment.
You'll architect and support robust pipelines for sensor data analysis, shaping ML systems that deliver real-time inference and anomaly detection on resource-constrained hardware. Collaborating closely with hardware, firmware, and platform teams, you'll be responsible for seamlessly integrating and validating intelligent features within embedded environments. This role will involve building and automating MLOps solutions, overseeing experiment management, and ensuring scalable and compliant machine learning lifecycle practices suited to regulatory demands.
Key Responsibilities
  • Lead the implementation of sensor data pipelines-from requirements through real-world integration
  • Design, train, optimize, and deploy ML models specifically for edge or embedded platforms, focusing on performance and reliability
  • Combine deep experience in Python with one or more frameworks (PyTorch, TensorFlow) to deliver production-ready models
  • Partner with engineering and product teams to embed, monitor, and validate ML inference in device applications
  • Develop infrastructure and automations for experiment tracking, continuous training, evaluation, and secure model deployment
  • Maintain full documentation for all aspects of the ML lifecycle to enable audit, regulatory compliance, and operational excellence
Required Skills & Experience
  • Extensive experience working with sensor and streaming data in production settings
  • Expertise in Python and at least one major ML library (PyTorch, TensorFlow, or alternatives)
  • Practical experience deploying models on edge systems (e.g., TensorRT, ONNX, TFLite, or similar technologies)
  • Solid engineering background with proficiency in C or C++ for embedded systems collaboration
  • Familiarity with MLOps and model versioning, preferably in a regulated environment
Preferred Qualifications
  • 5+ years hands-on ML development, with significant experience in real-time or embedded applications
  • Background working on medical, wearable, or robotics platforms highly desirable
  • Demonstrated success working in cross-functional teams with hardware/firmware integration
  • Experience in high-growth companies or small teams
  • Bachelor's or higher in Computer Science, Engineering, or a related field
Daily Activities
  • This role is hands-on and project-focused, where you'll design new ML workflows, transform raw data, and continually iterate on model performance
  • You'll support firmware engineers by ensuring seamless embedding and runtime validation of ML features
  • Work with DevOps and MLOps teams to automate deployments and create scalable, reliable ML solutions
  • Troubleshoot model issues, and refine algorithms for speed, accuracy, and compliance
Compensation & Benefits
Eligible for performance-based bonus or commission
Comprehensive benefits package including:
  • Health, dental, and vision insurance
  • Paid holidays and vacation
  • 401(k) retirement plan with company match (if applicable)
Applicants must have current authorization to work in the US on a full-time basis, now and in the future

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