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Ml Inference Jobs in Dallas, TX (NOW HIRING)

eTeam

Machine Learning Platform Engineer

Richardson, TX · On-site

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 ...

USM

JB061761 - MLOps Engineer

Grapevine, TX · On-site

$80/hr

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

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 ...

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 ...

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 ...

Ruri Software Technologies LLC

ML Engineer

Irving, TX · On-site

Development and Implement data pipelines and ML pipelines to facilitate model inference (both Real-time and batch) * Analyze large, complex data sets to identify the most performant way to process ...

Toyota

Senior AI/ML Platform Engineer

Plano, TX · On-site

$100K - $137K/yr

As a Senior AI/ML Platform Engineer, you will design, build, and support scalable platform ... Develop reusable patterns for inference services, prompt flow integration, and performance tuning ...

Toyota

Lead AI/ML Platform Engineer

Plano, TX · On-site

$98K - $129K/yr

You will help enable secure, production-ready MLOps and LLMOps infrastructure that supports model training, inference, orchestration, and retrieval-augmented generation. The Lead AI/ML Platform ...

Programmers.io

Java with AI ML ENgineer

Dallas, TX · On-site

$51.25 - $70.25/hr

Familiarity with ML model lifecycle - from data ingestion, training, deployment, to real-time inference (MLOPS) * 2+ years hands-on experience with GCP, AWS, or Azure * 2+ years working with pub/sub ...

Apex Systems

AI/ML Engineer

Coppell, TX · On-site

Job#: 3042434 AI/ML Engineer Hybrid in Dallas, TX or Tampa, FL Overview We are seeking a passionate ... Ability to build asynchronous Python APIs or services for model inference and operational ...

New

Toyota

Security ML / AI Engineer, Lead

Plano, TX · On-site

Deploy and manage model inference endpoints across cloud ML services and container-based serving * Build the analyst feedback loop: approval/rejection signals in dashboards feeding back into ...

Tiger Analytics Inc.

ML Ops Architect

Dallas, TX · On-site

As a Senior ML OPS Engineer, you will be joining a team of experienced Machine Learning Engineers ... inference at scale. * Deploy and manage machine learning & data pipelines in production ...

Tiger Analytics Inc.

ML Ops Architect

Dallas, TX · Remote

As a Senior ML OPS Engineer, you will be joining a team of experienced Machine Learning Engineers ... inference at scale. * Deploy and manage machine learning & data pipelines in production ...

Cotality

Director, ML Engineering

Dallas, TX

... inference. * MLOps Excellence: Drive the adoption of CI/CD for ML (CT - Continuous Training ... ensuring robust model versioning, automated testing, and seamless deployment via Vertex AI or GKE.

Cotality

Director, ML Engineering

Dallas, TX · On-site

... inference. * MLOps Excellence: Drive the adoption of CI/CD for ML (CT - Continuous Training ... ensuring robust model versioning, automated testing, and seamless deployment via Vertex AI or GKE.

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All JobsMl Inference JobsMl Inference Jobs in Dallas, TX

Ml Inference information

See Dallas, TX salary details

$37.1K

$121.4K

$194.4K

How much do ml inference jobs pay per year?

As of Jul 17, 2026, the average yearly pay for ml inference in Dallas, TX is $121,417.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,400.00 and $134,500.00 per year, depending on experience, location, and employer.

What is a $900000 AI job?

A $900,000 AI job typically refers to high-level roles in artificial intelligence, such as senior machine learning engineers or AI research directors, often involving advanced skills in deep learning, data modeling, and programming with tools like Python and TensorFlow. These positions usually require extensive experience, specialized knowledge, and may include leadership responsibilities or strategic decision-making.

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 engineer makes $500,000 a year?

Senior machine learning engineers with extensive experience, advanced skills in deep learning, and expertise in deploying large-scale models can earn salaries approaching or exceeding $500,000 annually, especially in high-cost-of-living areas or top tech companies. Compensation often includes base salary, bonuses, and stock options, reflecting their specialized knowledge and impact on product development.

Which 3 jobs will survive AI?

Jobs involving Ml Inference, such as data scientists, machine learning engineers, and AI system architects, are likely to persist as they require specialized expertise in developing, deploying, and maintaining AI models. These roles demand critical thinking, domain knowledge, and skills in programming and data analysis that are less easily automated. Continuous learning and staying updated with AI tools and frameworks are essential for these professions to remain relevant.

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.

Will MLE be replaced by AI?

Machine Learning Engineers (MLEs) design, develop, and optimize AI models and systems. While AI automation tools can assist with certain tasks, MLEs are essential for building, tuning, and maintaining complex models, making complete replacement unlikely in the near term. Their expertise in data handling, model deployment, and system integration remains critical in AI development environments.

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.
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What cities near Dallas, TX are hiring for Ml Inference jobs? Cities near Dallas, TX with the most Ml Inference job openings:
Ml Inference jobs near you
Machine Learning (ML) -Irving,TX-W2 Role

Machine Learning (ML) -Irving,TX-W2 Role

SSTech LLC

Irving, TX • On-site

Other

Posted 2 days ago

New

Job description

Need on w2 


Role :Data & Machine Learning Engineer

Remote role 

Job Details
Skills
Google Cloud
Google Cloud Platform
Java

Role *Machine Learning (ML)* 

Remote 

Systems Modeling
Streaming
Debugging

Show less
Summary
Introduction: The System Engineer will be responsible for working on the primary platform of Google Cloud Platform, deploying models, and integrating them into production applications and Java-based streaming pipelines. They will also be involved in monitoring model behavior in production, benchmarking, and performance testing.

Responsibilities:

Evaluate and benchmark new ML inference frameworks
Deploy models to Google Cloud Platform and integrate them into production applications and Java-based streaming pipelines
Own deployment automation end-to-end
Monitor model behavior in production for real end-users
Design and execute benchmarking, performance testing, and quality testing on ML models
Perform model sampling to support quality evaluation and researcher feedback loops
Debug issues across the full stack
Partner with ML researchers to provide benchmarking feedback and guide inference decisions
Adapt rapidly to non-standard and evolving tech stacks across hybrid infrastructure
 

Requirements:

Bachelor''''s or Master''''s degree in Computer Science, Computer or Electrical Engineering, Mathematics, or a related field
Strong foundation in ML inference, deployment, and quality testing
Demonstrated ability to ramp up quickly on new and unfamiliar tech stacks
End-to-end problem-solving mindset
Core ML knowledge sufficient to benchmark models and collaborate with researchers
Experience deploying models in cloud environments, ideally Google Cloud Platform
 

Good to Have:

Exposure to Java or JVM-based systems
Familiarity with streaming data architectures
Experience in hybrid cloud/on-prem environments

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