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Mlops Machine Learning Engineer Jobs in Austin, TX

Machine Learning Engineer

Austin, TX · On-site

$140K - $180K/yr

Python | AWS | Kubernetes | Docker | Terraform | Airflow | Spark | MLflow | Databricks | Kafka | CI/CD We're interested in people from backgrounds such as: ✔ Machine Learning EngineeringMLOps ...

Experience working in MLOps and building pipelines for deploying ML models * Programming skills in Python, C/C++ or Javascript * Understanding of machine learning and deep learning fundamentals

Experience working in MLOps and building pipelines for deploying ML models * Programming skills in Python, C/C++ or Javascript * Understanding of machine learning and deep learning fundamentals

Sr. Machine Learning Engineer

Austin, TX · On-site

$121K - $160K/yr

As a Senior Machine Learning Engineer, you'll take a leading technical role in building the ... Build and maintain CI/CD pipelines and MLOps workflows using Metaflow and ArgoCD, ensuring reliable ...

Avride develops autonomous vehicle and delivery robot technology, and they are seeking an experienced Machine Learning Engineer to enhance their autonomous systems. The role involves developing and ...

About the role We are looking for an experienced Machine Learning Engineer with a strong background in developing and deploying modern machine learning solutions for complex real-world challenges. In ...

We are looking for a passionate, highly motivated, and hands-on applied Machine Learning Engineer. This role will assist our Online Retail Decision Automation team by helping to research and develop ...

About the role We are looking for an experienced Machine Learning Engineer with a strong background in developing and deploying modern machine learning solutions for complex real-world challenges. In ...

SUMMARY The Machine Learning Engineer provides hands-on expertise in designing, implementing, and scaling AI solutions, while collaborating with cross-functional teams to advance machine learning ...

About the Role As a Machine Learning Engineer at Shipwell, you'll play a pivotal role in building and scaling our AI-powered logistics solutions. You'll design, develop, and maintain the data ...

SUMMARY The Machine Learning Engineer provides hands-on expertise in designing, implementing, and scaling AI solutions, while collaborating with cross-functional teams to advance machine learning ...

We are looking for a passionate, highly motivated, and hands-on applied Machine Learning Engineer. This role will assist our Online Retail Decision Automation team by helping to research and develop ...

We are looking for a passionate, highly motivated, and hands-on applied Machine Learning Engineer. This role will assist our Online Retail Decision Automation team by helping to research and develop ...

We are looking for a passionate, highly motivated, and hands-on applied Machine Learning Engineer. This role will assist our Online Retail Decision Automation team by helping to research and develop ...

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Mlops Machine Learning Engineer information

See Austin, TX salary details

$31.2K

$127.6K

$191.8K

How much do mlops machine learning engineer jobs pay per year?

As of Jul 19, 2026, the average yearly pay for mlops machine learning engineer in Austin, TX is $127,637.00, according to ZipRecruiter salary data. Most workers in this role earn between $100,600.00 and $153,600.00 per year, depending on experience, location, and employer.

What does an MLOps Machine Learning Engineer do?

An MLOps Machine Learning Engineer bridges the gap between data science and IT operations by developing, deploying, and maintaining machine learning models in production environments. They are responsible for automating workflows, managing model versioning, monitoring performance, and ensuring scalability and reliability of ML systems. Their work enables organizations to deploy machine learning solutions efficiently and consistently, making it easier to update and manage models as business needs evolve.

How does an MLOps Machine Learning Engineer typically collaborate with data scientists and software engineers during the deployment of machine learning models?

An MLOps Machine Learning Engineer acts as a bridge between data scientists and software engineers, ensuring machine learning models transition smoothly from development to production. They often work closely with data scientists to understand model requirements, data pipelines, and performance metrics, while also collaborating with software engineers to integrate models into scalable systems. Regular communication, shared documentation, and joint troubleshooting sessions are common, as the role requires aligning model performance with system reliability and maintainability. This collaborative environment helps ensure that models are robust, scalable, and impactful in real-world applications.

What is the difference between Mlops Machine Learning Engineer vs Data Scientist?

AspectMlops Machine Learning EngineerData Scientist
Required CredentialsBachelor's or master's in CS, data science, or related fields; certifications in cloud platforms or MLOps toolsBachelor's or master's in statistics, data science, or related fields; certifications in data analysis or machine learning
Work EnvironmentFocus on deploying, maintaining, and scaling ML models in production environmentsFocus on data analysis, model development, and insights generation
Employer & Industry UsageTech companies, startups, enterprises implementing ML solutionsResearch institutions, analytics firms, tech companies for data insights

While both roles involve machine learning, Mlops Machine Learning Engineers specialize in deploying and maintaining models in production, ensuring scalability and reliability. Data Scientists primarily focus on developing models and analyzing data to generate insights. The roles often overlap but differ in their core responsibilities and work environments.

What are the key skills and qualifications needed to thrive as an MLOps Machine Learning Engineer, and why are they important?

To thrive as an MLOps Machine Learning Engineer, you need a strong background in machine learning concepts, software engineering, and cloud infrastructure, typically supported by a degree in computer science or a related field. Familiarity with tools like Docker, Kubernetes, CI/CD pipelines, cloud platforms (AWS, GCP, Azure), and certifications such as Google Professional Machine Learning Engineer are highly beneficial. Strong problem-solving abilities, collaboration, and communication skills help you work effectively across data science and engineering teams. These skills are essential for reliably deploying, monitoring, and maintaining scalable machine learning solutions in production environments.
What cities near Austin, TX are hiring for Mlops Machine Learning Engineer jobs? Cities near Austin, TX with the most Mlops Machine Learning Engineer job openings:

Machine Learning Engineer

X4 Engineering

Austin, TX • On-site

$140K - $180K/yr

Other

Posted 25 days ago


Job description

🚀 Machine Learning Engineer

📍 Austin, TX (Hybrid/Remote Considered)

💰 $140,000 - $180,000 Base


We're partnering with a fast-growing energy firm looking to hire a Machine Learning Engineer to join a highly technical platform engineering team supporting traders, analysts, and quantitative researchers.


This is not a pure data science role. We're looking for an engineer who enjoys building robust production systems, scaling data and ML infrastructure, and working closely with front-office stakeholders to deliver real business impact.


What you'll be doing:

  • Building and maintaining ML and data platforms used for forecasting, optimization, and trading workflows
  • Designing scalable cloud-native infrastructure and deployment pipelines
  • Productionizing quantitative models and analytics tools
  • Developing distributed data and compute systems
  • Working directly with traders and business users to deliver reliable solutions
  • Driving engineering best practices across CI/CD, observability, testing, and automation


Tech stack includes:

Python | AWS | Kubernetes | Docker | Terraform | Airflow | Spark | MLflow | Databricks | Kafka | CI/CD

We're interested in people from backgrounds such as:

✔ Machine Learning Engineering

✔ MLOps Engineering

✔ Platform Engineering

✔ Software Engineering (with ML/Data exposure)

✔ Quant Development

✔ Infrastructure Engineering


Ideal candidates will have strong Python skills, cloud and DevOps experience, and a track record of building production systems. Experience within energy, trading, forecasting, or quantitative environments is beneficial but not essential.


If you'd like to learn more, please send me a message or apply directly.


They prefer the role to be worked on a hybrid model of 1-2 days a week. Salary offered is $140,000-$180,000.