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Ml Inference Jobs in Texas (NOW HIRING)

Responsibilities : • Should have 7 years of experience with a strong foundation in ML inference, deployment, and quality validation. Should be capable of end-to-end ownership from model deployment ...

Staff AI/ML Engineer - AV ML Infra

Austin, TX

$218K - $335K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

AI Validation & Inference: Ensures robust model performance by running large-scale simulation workloads and managing reliable ML inference pipelines. * ML Compute: Streamlines andoptimizeslarge-scale ...

Senior AI/ML Engineer - AV Infra

Austin, TX

$170K - $261K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

AI Validation & Inference: Ensures robust model performance by running large-scale simulation workloads and managing reliable ML inference pipelines. * ML Compute: Streamlines andoptimizeslarge-scale ...

Shell scripting and automation Containerization and orchestration (Docker, Kubernetes) Building reliable, scalable backend systems for ML inference Preferred / Nice-to-Have Experience with computer ...

Senior ML Accelerator Engineer - GPU

Austin, TX · On-site

$170K - $258K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

About the Team The AI Kernels team builds highperformance GPU kernels and custom libraries that sit at the heart ofouronvehicle ML inference for ADAS and autonomous driving . We own making core AI ...

Drive improvements to ML features including model optimization, inference performance, and feature enhancements. * Production-Ready Solutions: Build and deploy production-ready ML solutions for ...

Senior Software Engineer

Austin, TX

$118K - $156K/yr

Architect and implement containerized AI/ML inference pipelines for deployment on both cloud infrastructure and edge devices (NVIDIA DGX hardware) * Isolate and address performance issues end-to-end ...

ML Features Solutions Engineer

Austin, TX · On-site

$81K - $109K/yr

Responsibilities : • Design and implement core ML features including model optimization, quantization, and inference enhancements • Optimize model performance for latency, throughput, and memory ...

ML Features Solutions Engineer

Austin, TX · On-site

$200K - $270K/yr

  • Medical

  • Dental

  • Vision

  • Life

Drive improvements to ML features including model optimization, inference performance, and feature enhancements. * Production-Ready Solutions: Build and deploy production-ready ML solutions for ...

Ai/ML Engineer

Dallas, TX · On-site

$85K - $107K/yr

This role is pivotal in enabling enterprise-scale ML and generative AI capabilities by building ... Design highly available and performant serving environments for LLM inference using Azure ...

... inference, and monitoring in production environments. • Participate in continuous improvement of the ML infrastructure and processes for scalability and performance. Qualifications : Required : • ...

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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 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 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 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.

Is ML inference a high paying job?

ML inference roles are generally well-paying, especially for those with skills in machine learning frameworks, programming, and cloud platforms. Salaries vary based on experience, location, and industry, but they tend to be higher than average for tech-related positions.

What job categories do people searching Ml Inference jobs in Texas look for?

The top searched job categories for Ml Inference jobs in Texas are:

What cities in Texas are hiring for Ml Inference jobs?

Cities in Texas with the most Ml Inference job openings:

Infographic showing various Ml Inference job openings in Texas as of August 2026, with employment types broken down into 44% Full Time, 11% Part Time, and 45% Contract. Highlights an 89% In-person, and 11% Remote job distribution.

Machine Learning Platform Engineer

eTeam

Richardson, TX • On-site

Full-time

Re-posted 4 days ago


Job description

Job Summary:
eTeam is seeking a Machine Learning Platform Engineer to work onsite in Richardson, TX. The role requires strong expertise in ML inference, deployment, and quality validation, with responsibilities including end-to-end ownership from model deployment to user impact.
Responsibilities:
• Should have 7 years of experience with a strong foundation in ML inference, deployment, and quality validation. Should be capable of end-to-end ownership from model deployment to user impact, with the ability to quickly adapt to new technologies.
• Should have strong expertise in ML benchmarking and collaboration, along with hands-on experience deploying models on cloud platforms, preferably GCP. Familiarity with Java/JVM-based systems for model integration, streaming data architectures, and hybrid (on-prem and cloud) environments is essential.
• Must possess solid system design and distributed systems knowledge for troubleshooting, and hands-on experience with ML frameworks such as TensorFlow, PyTorch, or JAX.
Qualifications:
Required:
• 7 years of experience with a strong foundation in ML inference, deployment, and quality validation.
• Capability of end-to-end ownership from model deployment to user impact.
• Ability to quickly adapt to new technologies.
• Strong expertise in ML benchmarking and collaboration.
• Hands-on experience deploying models on cloud platforms, preferably GCP.
• Familiarity with Java/JVM-based systems for model integration.
• Experience with streaming data architectures.
• Experience in hybrid (on-prem and cloud) environments.
• Solid system design and distributed systems knowledge for troubleshooting.
• Hands-on experience with ML frameworks such as TensorFlow, PyTorch, or JAX.
Company:
eTeam is a staffing agency that also provides payrolling services. Founded in 1999, the company is headquartered in Somerset, USA, with a team of 501-1000 employees. The company is currently Late Stage.