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Data Annotation Intern Jobs in Texas (NOW HIRING)

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Data Annotation Intern information

What is the difference between Data Annotation Intern vs Data Labeling Specialist?

AspectData Annotation InternData Labeling Specialist
CredentialsTypically pursuing or recent graduate in related fieldRelevant experience or certifications in data labeling
Work EnvironmentInternship setting, often in tech or AI companiesFull-time or freelance roles in data annotation projects
Industry UsageCommon in tech, AI, and machine learning industriesUsed across similar industries for data preparation
Job FocusLearning and assisting with data annotation tasksPerforming detailed data labeling and quality control

While both roles involve working with data annotation, a Data Annotation Intern is typically a beginner or student gaining experience, whereas a Data Labeling Specialist is a more experienced professional focused on precise data labeling tasks. Interns often work under supervision, while specialists handle independent projects.

What is a data annotation intern?

A data annotation intern is a temporary position where individuals label or categorize data, such as images, text, or videos, to help train machine learning models. The role typically involves using annotation tools and requires attention to detail to ensure data accuracy and quality.

Is data annotation real or fake?

Data annotation is a legitimate job role involving labeling data such as images, text, or videos to train machine learning models. It requires attention to detail and often involves using specialized tools; the work is real and essential for AI development.

Does data annotation actually pay?

Data annotation internships and entry-level roles typically offer compensation, with pay rates varying based on the company, location, and experience. Many companies pay hourly or project-based wages, and some may provide stipends or bonuses for completing annotation tasks using tools like labeling software. It is common for data annotation jobs to be paid positions, especially when performed on a regular or full-time basis.

Is it hard to get hired for data annotation?

Getting hired as a data annotation intern generally depends on the company's requirements, but the process is often straightforward with basic computer skills and attention to detail. Many positions are entry-level and do not require extensive experience or certifications, making them accessible to beginners. However, strong accuracy and familiarity with annotation tools can improve chances of selection.

What are some common challenges faced by Data Annotation Interns and how can they be overcome?

Data Annotation Interns often encounter challenges such as maintaining consistency and accuracy when labeling large volumes of data, especially when guidelines evolve or when dealing with ambiguous cases. To overcome these challenges, it's important to frequently review annotation guidelines, communicate proactively with supervisors or team members for clarification, and participate in regular quality checks. Collaborating with experienced annotators and leveraging feedback provided during peer reviews can also help interns improve their accuracy and efficiency.

What are Data Annotation Interns?

Data Annotation Interns are entry-level professionals who assist in labeling and categorizing data, such as images, audio, or text, to help train machine learning models. Their work is crucial for ensuring that AI systems can accurately interpret and process various types of data. Interns typically use specialized software tools to annotate data according to specific guidelines, and they may also help with data quality checks. This position is ideal for those interested in gaining experience in artificial intelligence, data science, or related fields.

What are the key skills and qualifications needed to thrive as a Data Annotation Intern, and why are they important?

To thrive as a Data Annotation Intern, you need strong attention to detail, basic computer literacy, and familiarity with data labeling concepts, often supported by a background in computer science or related fields. Familiarity with annotation tools like Labelbox, Supervisely, or CVAT and understanding of data formats such as JSON or XML are typically required. Effective communication, time management, and the ability to follow complex guidelines are important soft skills for this role. These skills ensure high-quality, consistent data labeling, which is crucial for training accurate machine learning models.
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Infographic showing various Data Annotation Intern job openings in Texas as of July 2026, with employment types broken down into 2% Locum Tenens, 36% Full Time, 25% Part Time, 1% Contract, 35% Nights, and 1% Summer. Highlights an 46% Physical, 1% Hybrid, and 53% Remote job distribution.
Software Engineer Intern - ML Systems

Software Engineer Intern - ML Systems

Apptronik

Austin, TX

Other

Posted yesterday

Job description

JOB SUMMARY
Apptronik is seeking a Software Engineering Intern to join our ML Ops team for a 12-week fall project. In this role, you will work at the intersection of robotics and applied machine learning - building data annotation tooling and optimizing ML models that run on humanoid hardware. You
will help close the loop between raw robot experience data and deployable, hardware-ready models for Apollo, Apptronik's humanoid robot.

You will take ownership of two interconnected workstreams: (1) building or extending data annotation tools that let the team efficiently label and curate robot experience data, and (2) applying ML model optimization techniques - quantization, distillation, and inference profiling - to improve the throughput and efficiency of models deployed on physical systems. You will work alongside the simulation engineering, data platform, and learning teams, and contribute directly to how Apptronik turns ML research into production robot behavior.

ESSENTIAL DUTIES AND RESPONSIBILITIES

  • Data Annotation Tooling: Design and implement tooling for efficient annotation and curation of robot experience data - including sensor observations, trajectories, and task outcomes - in formats compatible with the team's data lake (MCAP, S3/MinIO).
  • ML Model Optimization: Profile, quantize, and/or distill ML models (RL policies, VLA controllers, or action heads) to reduce inference latency and memory footprint for deployment on robot hardware.
  • Hardware-Aware Evaluation: Build evaluation harnesses that benchmark optimized model performance against baseline, tracking metrics relevant to physical deployment (latency, memory, task success rate).
  • Integration with Existing Infra: Connect annotation outputs and optimized model artifacts with the team's existing artifact storage (S3/MinIO), training pipelines, and Kubernetes-based execution environment.
  • Documentation & Handoff: Produce design docs, runbooks, and example configurations so tooling can be adopted by controls, learning, and data platform teams after the internship.

SKILLS AND REQUIREMENTS

  • Python Proficiency: Demonstrated ability to write clean, tested, maintainable code for ML tooling, data pipelines, and automation.
  • Linux & Development Tools: Comfortable in a Linux environment; competence with Git, Docker, and modern Python tooling (pytest, uv/poetry, type hints).
  • ML Framework Experience: Hands-on experience with PyTorch or similar; familiarity with model quantization (INT8/FP16), ONNX export, or TensorRT is a plus.
  • Robotics Background: Coursework or project experience with robotic systems - kinematics, control, sensors, or simulation (ROS, MuJoCo, Isaac Sim, Gazebo, or comparable).
  • Data Pipeline Exposure: Experience moving data between annotation, training, evaluation, and storage stages.
  • Annotation Tooling (preferred): Prior experience with data annotation workflows, labeling interfaces (Label Studio, CVAT, custom tooling), or human-in-the-loop data pipelines.
  • Reinforcement Learning or VLA Exposure (preferred): Familiarity with RL training loops, policy rollouts, or vision-language-action (VLA) models.

EDUCATION and/or EXPERIENCE

  • Current enrollment in a Bachelor's or Master's degree program in Computer Science, Electrical Engineering, Robotics, or a related field.
  • Experience with projects involving robotics, ML model deployment, data annotation, or
    developer tooling is ideal.

PHYSICAL REQUIREMENTS

  • Prolonged periods of sitting at a desk and working on a computer.
  • Must be able to lift 15 pounds at times.
  • Vision to read printed materials and a computer screen.
  • Hearing and speech to communicate.

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