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Full Time Machine Learning Data Annotation Jobs in Texas

Ascentt

Senior Machine Learning Engineer

Plano, TX · On-site

$100K - $137K/yr

Data Science / Engineering Employment Type: Full-time About the Role: We are looking for an experienced Senior Machine Learning Engineer with deep expertise in statistical and machine learning ...

CVS Health

Machine Learning Engineer

Irving, TX · On-site +1

$96K - $144K/yr

Contributing to large-scale applications development, data science, or data analytics projects; and ... This pay range represents the base hourly rate or base annual full-time salary for all positions in ...

Ascentt

Senior Machine Learning Engineer

Plano, TX · On-site

$100K - $137K/yr

We turn enterprise data into real-time decisions using advanced machine learning and GenAI. Our team solves hard engineering problems at scale, with real-world industry impact. We're hiring ...

Confie

Machine Learning Engineer

Addison, TX · On-site +1

$110K - $130K/yr

... data warehouse platform using the Snowpark framework Develop novel solutions using knowledge of the latest artificial intelligence/machine learning/natural language processing techniques and rigorous ...

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All JobsFull Time Machine Learning Data Annotation JobsFull Time Machine Learning Data Annotation Jobs in Texas

Full Time Machine Learning Data Annotation information

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 are Full Time Machine Learning Data Annotation jobs?

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 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 Texas? The most popular types of Machine Learning Data Annotation jobs in Texas are:
What are popular job titles related to Full Time Machine Learning Data Annotation jobs in Texas? For Full Time Machine Learning Data Annotation jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Full Time Machine Learning Data Annotation jobs in Texas look for? The top searched job categories for Full Time Machine Learning Data Annotation jobs in Texas are:
What cities in Texas are hiring for Full Time Machine Learning Data Annotation jobs? Cities in Texas with the most Full Time Machine Learning Data Annotation job openings:
Full Time Machine Learning Data Annotation jobs near you

Software Engineer, Machine Learning Infrastructure

Bot Auto

Houston, TX • On-site

$165K - $195K/yr

Full-time

Posted 20 days ago

Job description

Company Introduction
At Bot Auto, we are revolutionizing the transportation of goods with our cutting-edge autonomous trucks, enhancing the quality of life for communities around the globe. With the agility of a start-up and the wisdom of seasoned experts, Bot Auto boasts a team that has achieved numerous world-firsts and unparalleled innovations. United by a shared vision, we create miracles and propel the future of transportation. Join us and transform your dreams into reality.
We are seeking a highly skilled and motivated Software Engineer to design, develop, and scale our machine learning annotation, evaluation, and training infrastructure. This role is central to the quality and velocity of our perception and ML models - from curating and managing high-quality annotated datasets, to building robust evaluation pipelines that drive continuous model improvement. The ideal candidate combines strong systems engineering skills with a deep understanding of ML Workflows/Ops and large-scale data infrastructure.
Key Responsibilities
Machine Learning & Deep Learning Infrastructure
  • Evaluation Platform - Architect and own a scalable, end-to-end model evaluation platform for perception and prediction models central to autonomous driving. Define metrics, design for scale, and make results actionable for researchers.
  • Training Infrastructure - Partner with research scientists to optimize and scale distributed training workflows. Integrate experiment tracking and reproducibility into the model lifecycle from day one.
  • Dataset & Feature Store - Design and maintain a versioned, high-quality training data store that accelerates model development and supports rapid iteration.
  • ML Pipelines - Build automated pipelines spanning data preparation, model training, validation, and deployment - enabling fast experimentation and reproducible outcomes.
  • Annotation Platform - Contribute to tooling and infrastructure that powers high-throughput, high-accuracy data annotation at scale.
  • MLOps - Develop production ML services that treat models as products - with reliability, observability, and continuous improvement built in.

Data Infrastructure
  • Maintain and evolve a robust data storage and access layer (S3 data lake, Delta Lake) underpinning annotation, evaluation, and training workflows.
  • Build scalable, reliable data collection pipelines supporting diverse vehicle dispatch missions.
  • Develop foundational services and packages that provide clean, performant access to autonomous driving data across the stack.
Qualifications
Required:
  • Educational Background: Bachelor's or Master's in Computer Science, or equivalent practical experience.
  • Strong Programming Skills: Strong proficiency in Python; working knowledge of C++
  • ML/DL Infrastructure Experience - Demonstrated hands-on experience building or scaling at least one of the following in a production environment:
    • Evaluation platforms - automated model benchmarking, metric computation, and regression tracking across model versions.
    • Training infrastructure - distributed training pipelines, experiment tracking, and model lifecycle management (e.g. W&B, MLflow, ClearML).
    • Dataset curation & feature stores - versioned dataset management, data lineage, and tooling for high-quality training data at scale.
    • Annotation platforms - tooling or pipelines that support high-throughput, high-accuracy labeling workflows.
  • Distributed Systems - Strong experience with distributed computing and container orchestration - Kubernetes, Spark, or comparable frameworks.
  • Ability to operate independently: scope ambiguous problems, make sound architecture decisions, and drive them to completion.

Preferred:
  • C++ experience in performance-sensitive or safety-critical applications
  • Full-stack service development experience.
  • Prior work in autonomous driving or robotics.

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