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Ml Inference Jobs in Texas (NOW HIRING)

We own the compiler that turns highlevel models into fast, reliable inference across GPUs powering ... Experience with ML frameworks (e.g.,PyTorch, TensorFlow, JAX) and software stack (e.g.,ONNX,MLIR ...

VP of Engineering

Austin, TX · Remote

$200K - $230K/yr

Familiarity with ontology/asset modeling, multimodal ML pipelines, and production ML inference integration. * Defense tech or other high-stakes, complex systems background. NOT A FIT IF: * Experience ...

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 ...

Implement retrieval strategies, prompt chaining, and inference orchestration for production use ... Strong Python skills and familiarity with ML/LLM frameworks (PyTorch, Transformers, LangChain ...

Deep familiarity with AI/ML inference stacks (ONNX Runtime, PyTorch, TensorRT-equivalent ecosystems, etc.) * Experience with GPU computing frameworks (ROCm strongly preferred; CUDA familiarity useful)

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 ...

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Ml Inference information

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.
What cities in Texas are hiring for Ml Inference jobs? Cities in Texas with the most Ml Inference job openings:
Staff ML Compiler Engineer

Staff ML Compiler Engineer

General Motors

Austin, TX • On-site

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 12 days ago


General Motors rating

8.0

Company rating: 8.0 out of 10

General Motors

Based on 309 frontline employees who took The Breakroom Quiz

7.4

Company rating compared to similar companies: 7.4 out of 10

Automakers average

Based on 6,228 frontline employees who took The Breakroom Quiz


Job description

Job Description

AbouttheMission

GM's vision of Zero Crashes, Zero Emissions, and Zero Congestion guides everything we do in autonomous and assisted driving. The AV organization is building advanced automated driving technologies,including Level 4-capable fully self-drivingsystems,tomove us toward safer, more sustainable, and more accessible mobility.

For theAIKernels & Compilers team, that mission shows up in the details: turning cuttingedgeperception, prediction, and planning research into productiongrade software that can run efficiently and reliably on real vehicles at scale. We pioneernew approachesto model export,kernel development, and performance engineering so that every cycle on our accelerators translates into better situational awareness, faster reaction times, and more robust behavior on the road.

If you want your compiler andkernelsworktodirectlyinfluencehow automated vehicles understand and react to the world - whileoperatingatthesafety,reliabilityand scaleof a company like GM - this is where that impact becomes real.

About the Team

The AI Compiler team sits at the heart of how advanced AI models make it onto the car. We own the compiler that turns highlevel models into fast, reliable inference across GPUs powering GM's nextgeneration autonomous and assisted driving features.

Our work spans graph lowering, operator coverage, kernel integration, and deployment tooling, with a mandate to squeeze every millisecond out of onvehicle workloads while preserving correctness and robustness in realworld conditions. We partner closely withAI Deployments,AI Solutions, Runtime, andAI Kernelsteams to codesignaplatform thatenablesnew ideasin researchto bequickly and safelyshippedto production fleets.

You'lljoin a group ofdeepcompiler, systems, and GPU engineerswho enjoyworking onhard problems, diving into MLIR/ONNXand CUDA/TensorRTinternals, and mentoring others on performance engineering. We value clear thinking, strong engineering fundamentals, and a culture where people can do the best work of their careers on problems that directly shape the future of automated driving.

The Role

As a Staff Compiler Engineer on the AI Kernels & Compilers team, you will own the endtoend compilation stack that takes highlevel models and turns them into highly optimized inference artifacts running on GM's autonomous and assisted driving platforms.You'lldefine the technical vision and build the tooling that makes that path fast, reliable, andeffortlessfor ML engineers across the AV organizationto compile their models.

You will design and evolve ourmodel export and compilation pipeline-from capturing highlevel model graphs, through intermediate representations and compiler transforms, into acceleratorspecific inference engines and their integration with our runtime-so that we can simultaneouslyoptimizecompilation throughput, model fidelity, and onvehicle latency. Along the way,you'llbuild robust tooling tovalidatenumerical correctness,detectand bisect performance regressions, and surface clear, actionable diagnostics back to model authors.

If you want to work at the intersection ofcompilers, performance engineering, and realworld autonomy, this role puts your decisions directly on the critical path of what runs on the car.

WhatYou'llDo

  • Own and evolvethe model compilation toolchain used to deploy largescaleperception, prediction, and planning models to the AV.

  • Architect new compiler passes and analysisthat improve build times, memory footprint, and runtime latency while preserving-or intentionally trading off-fidelity under strict safety and reliability constraints.

  • Collaborate closely with kernels, runtime, and hardware teamsto codesign interfaces, shape accelerator capabilities, and ensure the compiler exposes the right abstractions to unlock peak performance on each platform.

  • Set standards and best practicesfor model export, validation, and debugging so that AV teams can iterate quickly with clear, reproducible performance and accuracy characteristics.

Your Skills & Abilities (Required Qualifications)

  • 5+ years of experience in the field of compilers

  • Experience with ML frameworks (e.g.,PyTorch, TensorFlow, JAX) and software stack (e.g.,ONNX,MLIR, XLA, TVM,TensorRT,etc)

  • Expertisein writing production quality Python/C++ code

  • Expertisein the software development life-cycle - coding, debugging, optimization, testing, integration

  • BS, or higher degree, in CS/CE/EE, or equivalent

What Will Give YouACompetitive Edge (Preferred Qualifications)

  • Experience building andoptimizingONNXbasedmodelexport and deployment pipelines

  • GPU programming (CUDA) and familiarity with ML SW stack (e.g.,cuDNN,cuBLAS)

  • Experience with ML accelerators and hardware architecture

  • Experience developing and deploying machine learning models

Compensation: The compensation information is a good faith estimate only. It is based on what a successful applicant might be paid in accordance with applicable state laws. The compensation may not be representative for positions located outside of New York, Colorado, California, or Washington.

  • The salary range for this role: is$185,100 to $335,300. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position.

  • Bonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance.

  • Benefits: GM offers a variety of health and wellbeing benefit programs. Benefit options include medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation & holidays, tuition assistance programs, employee assistance program, GM vehicle discounts and more.

Company Vehicle: Upon successful completion of a motor vehicle report review, you will be eligible to participate in a company vehicle evaluation program, through which you will be assigned a General Motors vehicle to drive and evaluate. Note: program participants are required to purchase/lease a qualifying GM vehicle every four years unless one of a limited number of exceptions applies.

#GM-AV-1

This role is based remotely, but if the selected candidate lives within a specific mile radius of a GM hub, they will be expected to report to the location three times a week {or other frequency dictated by your manager}. The selected candidate will be required to travel <25% for this role. This job may be eligible for relocation benefits.

About GM

Our vision is a world with Zero Crashes, Zero Emissions and Zero Congestion and we embrace the responsibility to lead the change that will make our world better, safer and more equitable for all.

Why Join Us

We believe we all must make a choice every day - individually and collectively - to drive meaningful change through our words, our deeds and our culture. Every day, we want every employee to feel they belong to one General Motors team.

Benefits Overview

From day one, we're looking out for your well-being-at work and at home-so you can focus on realizing your ambitions. Learn how GM supports a rewarding career that rewards you personally by visiting Total Rewards resources.

Non-Discrimination and Equal Employment Opportunities (U.S.)

General Motors is committed to being a workplace that is not only free of unlawful discrimination, but one that genuinely fosters inclusion and belonging. We strongly believe that providing an inclusive workplace creates an environment in which our employees can thrive and develop better products for our customers.

All employment decisions are made on a non-discriminatory basis without regard to sex, race, color, national origin, citizenship status, religion, age, disability, pregnancy or maternity status, sexual orientation, gender identity, status as a veteran or protected veteran, or any other similarly protected status in accordance with federal, state and local laws.

We encourage interested candidates to review the key responsibilities and qualifications for each role and apply for any positions that match their skills and capabilities. Applicants in the recruitment process may be required, where applicable, to successfully complete a role-related assessment(s) and/or a pre-employment screening prior to beginning employment. To learn more, visit How we Hire.

Accommodations

General Motors offers opportunities to all job seekers including individuals with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, email us or call us at 1-800-865-7580. In your email, please include a description of the specific accommodation you are requesting as well as the job title and requisition number of the position for which you are applying.


Working at General Motors


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General Motors logo

About General Motors

Sourced by ZipRecruiter

General Motors is a company with global scale and capabilities, headquartered in Detroit, Michigan, with employees around the world. The company employs over 165,000 people, serves six continents, operates across 22 time zones, and has a diverse workforce speaking 75 languages1. GM’s vision is to drive the world forward by pioneering innovations that move and connect people to what matters. The company is working towards an all-electric future with its new Ultium Platform and is pushing transportation options beyond our wildest imaginations with autonomous vehicles. GM is also committed to becoming the most inclusive company in the world.

Industry

Transportation equipment manufacturing

Company size

10,000+ Employees

Headquarters location

Detroit, MI, US

Year founded

1908