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Software Engineer Ml Jobs in Texas (NOW HIRING)

Avride

Software Engineer - ML Platform

Austin, TX · On-site

We sit between Cloud Platform and ML engineers, turning low-level compute, storage, and networking primitives into an ML platform that teams actually use - scalable orchestration, distributed compute ...

Avride

Software Engineer - ML Platform

Austin, TX · On-site

We sit between Cloud Platform and ML engineers, turning low-level compute, storage, and networking primitives into an ML platform that teams actually use - scalable orchestration, distributed compute ...

Scalence L.L.C.

AI ML Software Engineer

Houston, TX · On-site

C. is a company focused on AI and machine learning solutions, and they are seeking an AI ML Software Engineer. The role involves designing, developing, and deploying AI/ML solutions while ...

Donato Technologies, Inc

Site Reliability Engineer

Austin, TX · On-site

$56.50 - $75/hr

Site Reliability Engineer SRE - ML platform Location: Austin, TX OR Sunnyvale, CA Title: Site ... Ability to understand tools used by data scientists and experience with software development and ...

Xforia, Inc.

Site Reliability Engineer

Austin, TX · On-site

$140K/yr

Site Reliability Engineer SRE - ML platform Location: Austin, TX OR Sunnyvale, CA Type: FTE Salary ... Ability to understand tools used by data scientists and experience with software development and ...

Hexagon AB

Principal AI/ML Software Engineer

Houston, TX · On-site

$128K - $172K/yr

Principal AI/ML Software Engineer Job Location (Short): Houston, Texas-USA | Madison, Alabama-USA Workplace Type: Remote Req Id: 2909 Responsibilities Position Overview We are seeking a motivated AI ...

Eden Technologies Inc

Software Engineer

Lockhart, TX · On-site

$130K - $165K/yr

Proficiency in Python for data processing, automation, test tooling, or ML/AI workflows ... Familiarity with core software engineering practices including version control, testing ...

webAI

AI Software Engineer

Austin, TX · On-site

The AI Software Engineer will focus on optimizing machine learning models for efficiency and ... Build and maintain ML frameworks that enable the AI Scientist's experiments at scale. • ...

WTS Paradigm LLC

Senior ML Ops Engineer

Irving, TX · On-site

$123K - $170K/yr

Paradigm is a software company transforming the way that the residential, construction & building product industries operate across the globe. We are looking for a Senior ML Ops Engineer to be part ...

Hp

Principal Software ML Engineer

Spring, TX

$120K - $161K/yr

Principal Software ML Engineer Description - HP is seeking a Principal Software ML Engineer to define and guide the architecture of modern software platforms within HP's device and services ecosystem.

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All JobsSoftware Engineer Ml JobsSoftware Engineer Ml Jobs in Texas

Software Engineer Ml information

See Texas salary details

$59.2K

$137.4K

$191.5K

How much do software engineer ml jobs pay per year?

As of Aug 3, 2026, the average yearly pay for software engineer ml in Texas is $137,441.00, according to ZipRecruiter salary data. Most workers in this role earn between $111,800.00 and $161,200.00 per year, depending on experience, location, and employer.

What does a software engineer ML do?

A Software Engineer, ML (Machine Learning) designs, develops, and deploys software systems that use machine learning algorithms to solve complex problems. They work on tasks such as building data pipelines, training and testing machine learning models, and integrating these models into production applications. They collaborate closely with data scientists, product managers, and other engineers to ensure that ML systems are scalable, efficient, and meet business objectives. Their work often involves programming, data analysis, and staying up-to-date with the latest developments in AI and machine learning.

What are some common challenges faced by software engineers working in machine learning, and how can they be addressed?

Software Engineers in Machine Learning often encounter challenges such as managing large datasets, ensuring model accuracy, and keeping up with rapidly evolving frameworks and tools. Collaboration with data scientists and domain experts is essential to align technical solutions with business goals. Staying current through continuous learning and leveraging cloud-based platforms or MLOps practices can help streamline workflows and improve model deployment. Additionally, effective communication within cross-functional teams is crucial for addressing both technical and non-technical challenges.

Are software engineer ML still in demand?

Software engineers specializing in machine learning are in high demand due to the growth of AI and data-driven applications. Skills in programming, data analysis, and frameworks like TensorFlow or PyTorch are valuable, and the role often requires staying current with evolving technologies. The demand is expected to continue as organizations increasingly adopt AI solutions.

What are the key skills and qualifications needed to thrive as a software engineer ML?

To thrive as a Software Engineer ML, you need strong proficiency in programming (especially Python), algorithms, machine learning theory, and a relevant degree in computer science or a related field. Experience with ML frameworks like TensorFlow or PyTorch, and familiarity with cloud computing platforms and version control systems are typically required. Analytical thinking, problem-solving, and effective communication skills help you stand out in collaborative and complex project environments. These skills are vital to efficiently develop, deploy, and maintain robust machine learning solutions that drive business value.
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Software Engineer Ml jobs near you
Infographic showing various Software Engineer Ml job openings in Texas as of July 2026, with employment types broken down into 92% Full Time, 5% Part Time, and 3% Contract. Highlights an 87% Physical, 5% Hybrid, and 8% Remote job distribution, with an average salary of $137,441 per year, or $66.1 per hour.

Software Engineer - ML Platform

Avride

Austin, TX • On-site

Other

Re-posted 3 days ago

Job description

About the team

The ML Platform team at Avride builds the infrastructure that powers large-scale ML training and data processing for autonomous driving. We sit between Cloud Platform and ML engineers, turning low-level compute, storage, and networking primitives into an ML platform that teams actually use - scalable orchestration, distributed compute, and production-grade tooling for the full model lifecycle.

About the role

As an ML Platform Engineer at Avride, you'll own critical pieces of the ML stack: workflow orchestration, distributed execution, resource governance, performance.You will shape how ML teams across the company run experiments and train models at scale. You will build the abstractions and services that make training workloads reliable, cost-efficient, and fast, helping ML teams run at scale on Kubernetes with strong reliability and excellent developer experience.

What you will do
  • Build and scale our ML compute platform on Kubernetes, using Argo Workflows for training, evaluation, and data processing orchestration
  • Design and implement core platform capabilities, including a Ray-based internal SDK for distributed execution, and multi-tenant resource governance - scheduling, priorities, quotas, and policy enforcement across GPU, CPU, memory, and IO
  • Improve end-to-end training throughput and platform efficiency by optimizing data access patterns, caching, and removing bottlenecks in storage, network, and resource contention
  • Work directly with ML teams to debug complex workload issues, drive root-cause analysis, and turn recurring problems into platform-level fixes
  • Evaluate, integrate and extend open-source tooling (Argo Workflows, Ray, Kubernetes ecosystem) to meet evolving platform needs
What you will need
  • Strong proficiency in Python or Go; C++ is a plus
  • Track record of designing and building scalable, maintainable systems and services
  • Experience operating production services end-to-end: APIs, reliability practices, observability
  • Deep knowledge of Kubernetes: how scheduling, resource management, controllers, and pod lifecycle actually behave under pressure
  • Solid Linux and systems debugging skills: performance investigation, networking, storage/IO
  • Ability to troubleshoot complex production issues across logs, metrics, and traces and drive them to resolution
Nice to have
  • Experience with Argo Workflows, Ray, MLflow, or comparable distributed ML tooling
  • Hands-on experience building or operating large-scale ML training systems: GPU scheduling, distributed training, training data pipelines
  • Track record of optimizing resource usage and performance in distributed environments

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