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Internship Data Annotation Analyst Jobs in Texas

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Data Scientist II

Austin, TX · On-site

... annotation guidelines and ensuring label quality. • Evaluate and apply the appropriate approach ... analysis of system performance to surface failure modes, data gaps, and high-value areas for ...

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Internship Data Annotation Analyst information

What is the difference between Internship Data Annotation Analyst vs Data Labeling Specialist?

AspectInternship Data Annotation AnalystData Labeling Specialist
CredentialsTypically pursuing or recent graduate in related fieldRelevant certifications or experience preferred
Work EnvironmentInternship setting, often in tech or AI companiesFull-time or freelance roles in data annotation companies
Industry UsageCommon in AI, machine learning, and tech industriesUsed across AI, autonomous vehicles, and healthcare sectors

The Internship Data Annotation Analyst is usually an entry-level role for students or recent graduates gaining experience in data annotation. In contrast, Data Labeling Specialists are more experienced professionals focused on accurately labeling data for AI models. Both roles are essential in AI development, but the internship provides learning opportunities, while the specialist role involves more independent work and expertise.

What are the most commonly searched types of Data Annotation Analyst jobs in Texas? The most popular types of Data Annotation Analyst jobs in Texas are:
What cities in Texas are hiring for Internship Data Annotation Analyst jobs? Cities in Texas with the most Internship Data Annotation Analyst job openings:
Internship Data Annotation Analyst jobs near you

Software Engineer Intern - ML Systems

Apptronik

Austin, TX • On-site

Other

Posted 7 days ago

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 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 o: Familiarity with RL training loops, policy rollouts 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.

Apply