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Ml Inference Jobs in Arizona (NOW HIRING)

Shutterfly

Senior Principal Software Engineer

Phoenix, AZ · Remote

$122K - $169K/yr

Scalable inference and orchestration infrastructure * Ensure systems are highly available ... Experience building AI/ML systems, especially with: * LLMs, agent frameworks, and retrieval systems

Shutterfly

Senior Principal Software Engineer

Tempe, AZ · Remote

$122K - $168K/yr

Scalable inference and orchestration infrastructure * Ensure systems are highly available ... Experience building AI/ML systems, especially with: * LLMs, agent frameworks, and retrieval systems

State Farm

Lead GenAI Engineer (LLM)

Tempe, AZ · Hybrid

$98K - $129K/yr

Engineer Data & Analytics Pipelines -- Build pipelines that feed AI/ML systems with clean, governed ... behavior and inference performance * Enhance User Experience -- Design intuitive AI-powered ...

Shutterfly

Senior Principal Software Engineer

Phoenix, AZ · Remote

$122K - $169K/yr

Scalable inference and orchestration infrastructure * Ensure systems are highly available ... Experience building AI/ML systems, especially with: * LLMs, agent frameworks, and retrieval systems

University of Arizona

Scientific Analyst II

Tucson, AZ · On-site

The primary deliverables of this role are computational pipelines, ML models, and exploratory data ... causal inference. Scientific Communication, Dissemination, and Collaboration: * Compare and ...

Shutterfly

Senior Principal Software Engineer

Tempe, AZ · Remote

$122K - $168K/yr

Scalable inference and orchestration infrastructure * Ensure systems are highly available ... Experience building AI/ML systems, especially with: * LLMs, agent frameworks, and retrieval systems

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Ml Inference information

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.

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Ml Inference jobs near you

Senior / Staff ML Onboard Optimization Engineer

Waabi

Phoenix, AZ • On-site, Remote

$141K - $249K/yr

Full-time

Medical, Dental, Vision, PTO

Re-posted 2 days ago

Job description

Waabi, founded by AI visionary Raquel Urtasun, is the leader in Physical AI. With a world-class team, we're unlocking the next era of autonomous transportation with technology that's powering commercial autonomous trucks and robotaxis. Waabi is backed by and partners with world leaders in AI, automotive, logistics, and deep tech.

With offices in Toronto, San Francisco, Dallas, and Pittsburgh, Waabi is growing quickly and looking for diverse, innovative and collaborative candidates who want to impact the world in a positive way. To learn more visit: www.waabi.ai

You will...
- Collaborate closely with autonomy and algorithm engineers to scale safe self-driving systems using an AI-first approach.
- Expand the model deployment pipeline to new GPUs and embedded systems for the next generation of our onboard compute system.
- Use frameworks such as TensorRT and modelopt to optimize the models running on the truck.
- Create and benchmark new CUDA kernels for inference.
- Comprehensively profile model runtime and memory to pinpoint performance bottlenecks.
 
Qualifications:
- MS/PhD or Bachelors degree with a minimum of 6 years of industry experience in Computer Science, Robotics and/or similar technical field(s) of study.
- Solid coding proficiency in a variety of coding languages including Python, C++ or Rust.
- Experience in deep learning frameworks such as PyTorch.
- Skilled in profiling CPU and GPU code using tools such as PyTorch Profiler and NVIDIA Nsight.
- Experience with Nvidia embedded platforms such as Nvidia Jetson or Thor.
- Open-minded and collaborative team player with willingness to help others.
- Passionate about self-driving technologies, solving hard problems, and creating innovative solutions.
 
Bonus/nice to have:
- Experience in model compilation and exporting, interaction with lower level concepts like TensorRT.
- Experience in identifying when custom CUDA kernels are needed, and implementing them.
- Experience in Bazel build systems, and integrating third party packages into dev environments.
The US yearly salary range for this role is: $141,000 - $249,000 in addition to competitive perks & benefits. Waabi's yearly salary ranges are determined based on several factors in accordance with the Company's compensation practices. The salary base range is reflective of the minimum and maximum target for new hire salaries for the position across all US locations.  Note: The Company provides additional compensation for employees in this role, including equity incentive awards and an annual performance bonus.

Perks/Benefits:
Waabi provides a competitive benefits package that includes:
- Competitive compensation and equity awards.
- Health and Wellness benefits encompassing Medical, Dental and Vision coverage.
- Unlimited Vacation.
- Flexible hours and Work from Home support.
- Daily drinks, snacks and catered meals (when in office).
- Regularly scheduled team building activities and social events both on-site, off-site & virtually.
- World-class facility that includes a gym, games room (ping pong table, video game consoles, board games, etc), multiple collaborative working spaces and a gorgeous patio!(when in office)
- As we grow, this list continues to evolve! 

Waabi is an equal opportunity employer that celebrates diversity and is committed to creating a supportive, inclusive, and accessible environment for all employees. If reasonable accommodation is needed to participate in the job application or interview process please let our recruiting team know.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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