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Ml Inference Jobs in Texas (NOW HIRING)

SEMRON

ML Research Scientist

Austin, TX · On-site

SEMRON is redefining what's possible in AI hardware, and they are seeking an ML Research Scientist to design algorithms and quantization schemes for efficient inference on their analog in-memory ...

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

$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 ...

ConnectedX, Inc.

ML Engineer

Plano, TX · On-site

ML Engineer Location: Remote -ML Engineer Job Summary We are seeking a talented AI/ML Engineer to ... Optimize model inference performance for scalability and reliability. · Document model ...

Hexagon AB

Principal AI/ML Software Engineer

Houston, TX · On-site

$128K - $172K/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

Staff ML Compiler Engineer

Austin, TX · On-site

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 ...

General Motors

Senior ML Compiler Engineer

Austin, TX

$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 ...

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) • ...

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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 are popular job titles related to Ml Inference jobs in Texas?

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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:

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.

ML Research Scientist

SEMRON

Austin, TX • On-site

Full-time

Re-posted 20 days ago

Job description

Job Summary:
SEMRON is redefining what’s possible in AI hardware, and they are seeking an ML Research Scientist to design algorithms and quantization schemes for efficient inference on their analog in-memory compute platform. The role involves researching novel quantization methods, designing algorithms for matrix-vector multiplication, and collaborating with hardware engineers to define algorithmic requirements.
Responsibilities:
• Research and develop novel analog-aware quantization methods (PTQ and QAT) tailored to in-memory compute constraints
• Design mathematically principled matrix-vector multiplication algorithms that exploit sparsity, noise resilience, and non-idealities to improve hardware efficiency
• Collaborate with analog hardware engineers to define algorithmic requirements and guide co-development of compute primitives
Qualifications:
Required:
• PhD or equivalent research experience in machine learning, applied mathematics, or a related field
• Strong understanding of quantization, model optimization, and numerical methods for DNNs
• Proficiency in Python and PyTorch, with the ability to rapidly prototype and evaluate research ideas
• A research mindset: curiosity, rigor, and the ability to explore and discard ideas efficiently
Preferred:
• Contributions to quantization libraries or novel compression methods
• Publications in top-tier ML venues (NeurIPS, ICLR, ICML, etc.)
• Familiarity with analog computation challenges (noise, nonlinearity, limited precision, etc.) and the ability to abstract them into robust algorithms
• Experience collaborating with hardware teams or formulating algorithm-hardware co-design strategies
Company:
SEMRON develops a 3D-scaled AI inference chip, based on a new proven semiconductor device. Founded in 2020, the company is headquartered in Dresden, DEU, with a team of 11-50 employees. The company is currently Early Stage.

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