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Ml Inference Jobs in Texas (NOW HIRING)

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

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

Mythic

Senior Silicon Emulation Engineer

Austin, TX

$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

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 ...

AMD

Software Solutions Architect

Austin, TX · On-site

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

Hexagon AB

Principal AI/ML Software Engineer

Houston, TX · On-site

$124K - $167K/yr

Strong foundation in statistics, A/B testing, causal inference, and experimental design • ... ML engineering, or related roles • 3+ years building NLP/generative AI applications and ...

General Motors

Senior ML Compiler Engineer

Austin, TX · On-site

$103K - $142K/yr

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 ...

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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.
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Ml Inference jobs near you
AI/ML Engineer

AI/ML Engineer

1 point system

Plano, TX • On-site

Contractor

Re-posted 19 days ago

Job description

Interview Process: In Person
Must Have Skills: 

  • Core Responsibilities (AI/ML, Python, AWS, GenAI)
  • Design and implement end-to-end AI/ML and Generative AI solutions using Python, including model training, evaluation, optimization, and deployment.
  • Build and maintain cloud‑native applications on AWS using services such as Lambda, ECS/Fargate, S3, API Gateway, DynamoDB, RDS/Aurora, SageMaker, and Bedrock.
  • Develop high‑performance Python microservices (FastAPI/Flask) enabling scalable data pipelines, model inference, and real‑time analytics.
  • Architect and operationalize RAG pipelines, embeddings, vector databases, and LLM‑powered automation (chatbots, summarization, semantic search, anomaly detection).
  • Implement CI/CD pipelines (GitHub/GitLab/CodePipeline) and infrastructure‑as‑code (Terraform/CloudFormation) for reliable, automated deployments.
  • Build robust MLOps workflows, including model versioning, containerized training/inference, automated retraining, monitoring, and performance tuning.