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Ml Inference Jobs in Utah (NOW HIRING)

Wsgr

Senior AI Security Engineer

Salt Lake City, UT · On-site +1

$163.20K - $220.80K/yr

Demonstrated experience with AI threat modeling - including OWASP LLM Top 10, adversarial ML attack ... AI inference endpoints, and identity-aware proxy patterns for LLM access control * Experience ...

LVT

Data Engineer, AI

American Fork, UT · On-site +1

$102.30K - $122.90K/yr

Collaborate with AI/ML engineers to support Snowflake Cortex model development with well-structured, context-rich training and inference data. * Partner with analytics engineers, data scientists, and ...

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Ml Inference information

What are the key skills and qualifications needed to thrive in ML Inference, and why are they important?

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 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 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
AI Engineer IV (Embedded Software)

AI Engineer IV (Embedded Software)

Autonomous Solutions

Lehi, UT • On-site

$145.53K - $169.79K/yr

Full-time

Posted 21 days ago

Job description

Autonomous Solutions Inc builds autonomous systems for fleets operating in demanding, high-risk, and repetitive environments. Founded in 2000, we've grown into one of the largest privately held robotics companies in the world, with deployments across heavy construction, agriculture, logistics, and landscaping. Our work is guided by seven core values: Simplicity, Safety, Transparency, Humility, Attention to Detail, Autonomy, and Growth. Being privately held means good ideas move quickly, ownership is real, and the work you do here shows up in the world.

As an AI Engineer IV (Embedded Software), you will deploy, optimize, and maintain AI capabilities on constrained hardware platforms within Autonomous Solutions Inc's robotics systems. You will ensure AI models operate reliably within tight compute and memory limits, integrating smoothly with firmware and robotics software. This role sits at the intersection of AI model deployment and embedded systems engineering, where real-time performance and hardware constraints matter as much as model accuracy.

Responsibilities

  • Deploy AI models onto constrained hardware and embedded platforms.

  • Optimize compute, memory, and power usage for real-time embedded operation.

  • Convert AI models into production-ready embedded libraries or hardware-optimized modules.

  • Tune real-time performance and latency to meet embedded system requirements.

  • Integrate AI models and algorithms that improve system efficiency and reliability.

  • Test and validate AI performance on embedded platforms for robustness and stability.

  • Collaborate with robotics, firmware, and software teams on embedded AI alignment.

  • Continuously improve embedded AI systems through updates, optimizations, and performance refinements.

Qualifications

  • Bachelor's degree in Computer Engineering, Electrical Engineering, Computer Science, or a related field.

  • 8+ years designing and implementing embedded real-time software systems using C/C++.

  • 3+ years deploying AI or ML models on embedded systems or constrained hardware.

  • Proficiency with ML inference runtimes such as TensorRT, ONNX Runtime, or TensorFlow Lite.

  • Experience optimizing compute, memory, and power for real-time embedded applications.

  • Familiarity with microcontrollers, embedded Linux, RTOS, or hardware accelerators.

  • Experience with large multithreaded embedded applications running on an RTOS.

Physical Requirements

  • Ability to remain in a stationary position at a computer workstation for extended periods.

  • Ability to operate a computer and other office productivity equipment continuously.

  • Ability to communicate and exchange information in person, via phone, and through electronic means.

  • Ability to traverse office, lab, data center, and field environments as required.

At Autonomous Solutions Inc, we are committed to fostering a diverse, inclusive, and equitable workplace where all employees and applicants have equal opportunities. We prohibit discrimination and harassment of any kind based on race, color, religion, sex, national origin, age, disability, genetic information, veteran status, sexual orientation, gender identity, or any other legally protected characteristic. Autonomous Solutions Inc complies with all applicable federal, state, and local laws regarding nondiscrimination in employment and is dedicated to providing reasonable accommodations for individuals with disabilities throughout the hiring process.


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