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Software Engineer Ml Jobs in Texas (NOW HIRING)

Avride

Software Engineer - ML Platform

Austin, TX

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 ...

CSAA

Job Title Software Engineer III - AI/ML Platform Operations - Remote Requisition Number R7739 Software Engineer III - AI/ML Platform Operations - Remote (Open) Location Arizona - Home Teleworkers ...

The Bronx

Software Engineer AI/ML

Addison, TX · On-site

$145 - $160/hr

Scion Staffing has been engaged to conduct a search for a Senior AI/ML Engineer for an innovative ... Collaborate closely with Data Engineering, Software Engineering, Security, and business teams to ...

J.P. Morgan

Software Engineer II - AI/ML Developer

Plano, TX · On-site

$93K - $127K/yr

As a Software Engineer II at JPMorganChase within the Consumer & Community Banking, you are part of ... Proficiency in Python and one or more ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn)

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Software Engineer Ml information

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$59.2K

$137.4K

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How much do software engineer ml jobs pay per year?

As of Aug 23, 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 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.

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.

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Software Engineer Ml jobs near you
Infographic showing various Software Engineer Ml job openings in Texas as of August 2026, with employment types broken down into 1% As Needed, 88% Full Time, 8% Part Time, and 3% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution, with an average salary of $137,441 per year, or $66.1 per hour.

Software Engineer - ML Platform

Avride

Austin, TX

Full-time

Re-posted 23 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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