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Ml Inference Jobs in Texas (NOW HIRING)

Rivago infotech inc

Lead ML Engineer

Manor, TX · On-site

$110K - $145K/yr

The role focuses on real-time inference, feature engineering, APIs, graph-based fraud detection ... Integrate ML models with REST APIs and microservices. * Support graph-based fraud detection using ...

Johnson Controls, Inc.

Ai/ML Engineer

Irving, TX · On-site

$85 - $107/hr

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 ...

Johnson Controls

Ai/ML Engineer

Dallas, TX

$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 ...

Catalyst Labs

ML Engineer

Austin, TX · On-site

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

Catalyst Labs

ML Engineer

San Antonio, TX · On-site

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

Catalyst Labs

ML Engineer

Fort Worth, TX · On-site

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

Catalyst Labs

ML Engineer

Houston, TX · On-site

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

Catalyst Labs

ML Engineer

Austin, TX · On-site

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

Mythic

Senior Silicon Emulation Engineer

Austin, TX · On-site

$120K - $225K/yr

Enable early firmware, driver, runtime, and ML stack bring-up on emulated hardware * Support execution of AI inference workloads (e.g., CNNs, transformers) on emulated Mythic accelerators

Catalyst Labs

ML Engineer

Dallas, TX · On-site

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

Johnson Controls

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 ...

Mythic

Senior Silicon Emulation Engineer

Austin, TX · On-site

$120K - $225K/yr

Enable early firmware, driver, runtime, and ML stack bring-up on emulated hardware * Support execution of AI inference workloads (e.g., CNNs, transformers) on emulated Mythic accelerators

1 point system

AI/ML Engineer

Plano, TX · On-site

Core Responsibilities (AI/ML, Python, AWS, GenAI) * Design and implement end-to-end AI/ML and ... Build robust MLOps workflows, including model versioning, containerized training/inference ...

Mythic

Senior Silicon Emulation Engineer

Austin, TX · On-site

$120K - $225K/yr

Enable early firmware, driver, runtime, and ML stack bring-up on emulated hardware * Support execution of AI inference workloads (e.g., CNNs, transformers) on emulated Mythic accelerators

ClifyX

Senior AI/ML Developer

Irving, TX · On-site

Preferred : • Financial domain expertise (risk, fraud, forecasting, customer intelligence). • Advanced ML topics: time series, graph ML, optimization, causal inference. • ONNX/TensorRT model ...

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

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Ml Inference jobs near you
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.

Lead Machine Learning Inference Engineer, Advertising

Roku

Austin, TX

$101K - $133K/yr

Full-time

Re-posted yesterday

Job description

About the team  

The Advertising Performance group focuses on performance for all participants in the Advertising ecosystem - Advertisers, Publishers, and Roku. The systems and solutions span multiple disciplines and technologies to perform real-time multi-objective optimization across distributed systems at large scale and with low latency. We use Machine Learning, Reinforcement Learning, AI, Control and Optimization Systems, and Auction Dynamics to solve a large set of complex problems. At the core of this is our Machine Learning and Inference Platform that powers the entire landscape. 

About the role  

In this role, you will architect, design, and lead the development of a SOTA Inference platform that can handle Advertising-level low latencies, scale, throughput, and availability with optimizations that span across hardware, software, and models. We're looking for a strong technical leader with deep experience in ML serving, high-performance computing, and industry standard frameworks - someone excited to mentor engineers, innovate at scale, and shape the future of machine learning at Roku.

What you'll be doing
  • Lead the design and development of a SOTA Inference platform  
  • Oversee the development of monitoring, observability, and other tooling to ensure system and model performance, reliability, and scalability of online inference services
  • Identify and resolve system inefficiencies, performance bottlenecks, and reliability issues, ensuring optimized end-to-end performance 
  • Stay at the forefront of advancements in inference frameworks, ML hardware acceleration,  and distributed systems, and incorporate innovations where and when they are impactful
We're excited if you have
  • M.S. or above in CS, ECE, or a related field 
  • 10+ years of experience in developing and deploying large-scale, distributed systems, with at least 5 years in a leadership or technical lead role  
  • Strong programming skills in high-performance languages
  • Deep understanding of inference frameworks and ML system deployment
  • Proven experience optimizing performance for large-scale machine learning systems, including a deep knowledge of SOTA model optimizations, hardware-software co-design, GPU acceleration, and HPC techniques
  • Excellent communication and collaboration skills
  • Experience leading teams working on high-throughput, low-latency ML serving systems
  • Experience collaborating with and leading global, cross-functional teams
  • Contributions to open-source ML or systems projects
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