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Full Time Machine Learning Data Annotation Jobs in Austin, TX

Senior Machine Learning Engineer

Austin, TX

$121K - $160K/yr

The Ad Performance team owns server technologies, data, and cloud services aimed at improving the ad experience. We're looking for seasoned engineers with a background in machine learning to aid in ...

Important: the data science internship does not necessarily transition to a full-time role at the end of the period. Depending on team need, firm budget, and fit, interns should not expect their time ...

Senior Machine Learning Engineer

Austin, TX · On-site

$121K - $160K/yr

Experience building data processing pipelines and large scale machine learning systems with experience in big data technologies like Spark, SQL, Snowflake/Hadoop, etc. Skilled in communication ...

Experience building data processing pipelines and large scale machine learning systems with ... experience in big data technologies like Spark, SQL, Snowflake/Hadoop, etc. Skilled in ...

Experience building data processing pipelines and large scale machine learning systems with ... experience in big data technologies like Spark, SQL, Snowflake/Hadoop, etc. Skilled in ...

Experience building data processing pipelines and large scale machine learning systems with ... experience in big data technologies like Spark, SQL, Snowflake/Hadoop, etc. Skilled in ...

Showing results 41-60

Full Time Machine Learning Data Annotation information

See Austin, TX salary details

$37.2K

$121.7K

$194.8K

How much do full time machine learning data annotation jobs pay per year?

As of Sep 4, 2026, the average yearly pay for full time machine learning data annotation in Austin, TX is $121,659.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,600.00 and $134,800.00 per year, depending on experience, location, and employer.

What is a full time machine learning data annotation job?

Full time machine learning data annotation jobs involve labeling, tagging, or categorizing data such as images, text, audio, or video to help train machine learning models. Data annotators play a crucial role in ensuring that AI systems learn from high-quality, accurately labeled datasets. These positions often require attention to detail, consistency, and sometimes familiarity with the subject matter or specialized tools. Full-time roles may be remote or onsite and can span industries like autonomous vehicles, healthcare, retail, and more.

What are some common challenges faced by machine learning data annotators, and how are these typically addressed within a team?

Machine learning data annotators often encounter challenges such as maintaining consistency in labeling, handling ambiguous data, and meeting tight deadlines for large datasets. Teams usually address these by establishing clear annotation guidelines, conducting regular training sessions, and implementing quality assurance processes like peer reviews and spot checks. Collaboration with data scientists and project managers is also common, ensuring that annotators can ask questions and clarify uncertainties, leading to higher-quality labeled data and a supportive work environment.

What are the key skills and qualifications needed to thrive as a full time machine learning data annotation specialist, and why are they important?

To thrive as a Full Time Machine Learning Data Annotation Specialist, you need strong attention to detail, basic data literacy, and familiarity with data labeling concepts, often supported by a high school diploma or equivalent. Proficiency in specialized annotation platforms, spreadsheet tools, and sometimes knowledge of Python or labeling frameworks is typically required. Reliability, patience, and effective communication are valuable soft skills for ensuring accuracy and collaborating with team members. These skills and qualities are crucial because they directly impact the quality of training data, which is essential for developing effective machine learning models.

What is the difference between Full Time Machine Learning Data Annotation vs Data Labeling Specialist?

AspectFull Time Machine Learning Data AnnotationData Labeling Specialist
CredentialsHigh school diploma or equivalent; some roles prefer technical certificationsHigh school diploma or equivalent; training often provided on the job
Work EnvironmentOffice or remote; collaborative with data science teamsRemote or office; focused on labeling tasks
Industry UsageUsed across AI/ML companies, tech firms, and startupsCommon in AI/ML, data services, and outsourcing companies
Job FocusCreating labeled datasets for machine learning modelsAnnotating data such as images, videos, or text for AI training

Full Time Machine Learning Data Annotation involves creating high-quality labeled datasets for AI models, often requiring technical understanding. Data Labeling Specialists focus on annotating data accurately, typically with less emphasis on technical skills. Both roles are essential in AI development but differ mainly in scope and technical complexity.

What are the most commonly searched types of Machine Learning Data Annotation jobs in Austin, TX?

The most popular types of Machine Learning Data Annotation jobs in Austin, TX are:

What are popular job titles related to Full Time Machine Learning Data Annotation jobs in Austin, TX?

For Full Time Machine Learning Data Annotation jobs in Austin, TX, the most frequently searched job titles are:

What job categories do people searching Full Time Machine Learning Data Annotation jobs in Austin, TX look for?

The top searched job categories for Full Time Machine Learning Data Annotation jobs in Austin, TX are:

What cities near Austin, TX are hiring for Full Time Machine Learning Data Annotation jobs?

Cities near Austin, TX with the most Full Time Machine Learning Data Annotation job openings:

Infographic showing various Full Time Machine Learning Data Annotation job openings in Austin, TX as of August 2026, with employment types broken down into 1% As Needed, 84% Full Time, 12% Part Time, and 3% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $121,659 per year, or $58.5 per hour.

Senior Machine Learning Engineer

Roku

Austin, TX

$121K - $160K/yr

Full-time

Re-posted 20 days ago


Key responsibilities

  • Build and maintain a machine learning platform that manages the entire model lifecycle, including feature engineering, training, versioning, deployment, and monitoring.

  • Apply expertise in data analysis and feature engineering to generate features for multiple use cases and models.

  • Develop and evaluate machine learning models using techniques such as Decision Trees, Logistic Regression, Neural Networks, and Bayesian Analysis for improving system performance and accuracy.


Job description

About the team 

The Advertising Performance group focuses on performance for all participants in the Advertising ecosystem - Advertisers, Publishers, and Roku. The systems and solutions span multiple disciplines and technologies to perform real-time multi-objective optimization across distributed systems at large scale and with low latency. We use Machine Learning, Reinforcement Learning, AI, Control and Optimization Systems, and Auction Dynamics to solve a large set of complex problems. At the core of this is our Machine Learning, Experimentation, and Inference Platform that powers the entire landscape, which we continuously evolve over time.

About the role 

We're on a mission to build cutting-edge advertising technology that empowers businesses to run sustainable and highly-profitable campaigns. The Ad Performance team owns server technologies, data, and cloud services aimed at improving the ad experience. We're looking for seasoned engineers with a background in machine learning to aid in this mission. Examples of problems include improving ad relevance, inferring demographics, yield optimization, and many more. Employees in this role are expected to apply knowledge of experimental methodologies, statistics, optimization, probability theory, and machine learning using both general purpose software and statistical languages.

What you'll be doing 
  • ML infrastructure: Help build a first-class machine learning platform from the ground up which manages the entire model lifecycle - feature engineering, model training, versioning, deployment, online serving/evaluation, and monitoring prediction quality
  • Data analysis and feature engineering: Apply your expertise to identify and generate features that can be leveraged by multiple use cases and models
  • Model training with batch and real-time prediction scenarios: Use machine learning and statistical modelling techniques such as Decision Trees, Logistic Regression, Neural Networks, Bayesian Analysis and others to develop and evaluate algorithms for improving product/system performance, quality, and accuracy
  • Production operations: Low-level systems debugging, performance measurement, and optimisation on large production clusters
  • Collaboration with cross-functional teams: Partner with product managers, data scientists, and other engineers to deliver impactful solutions
  • Staying ahead of the curve: Continuously learn and adapt to emerging technologies and industry trends
We're excited if you have 
  • Bachelors, Masters, or PhD in Computer Science, Statistics, or a related field
  • 5 years of experience in applied machine learning on real use cases 
  • Proficient coding skills and strong software development experience in Spark, Python, or Java
  • Familiarity with real-time evaluation of models with low latency constraints
  • Familiarity with distributed ML frameworks such as Spark-MLlib, TensorFlow, etc.
  • Ability to work with large scale computing frameworks, data analysis systems, and modelling environments i.e. Spark, Hive, NoSQL stores such as Aerospike and ScyllaDB
  • Ad Tech experience is preferred 
  • Proficient use of AI tools and agentic coding practices 
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