Machine Learning Engineer Intern at 1+1=3

Wayne State University; Austin (TX); On-site; USD 20,000 - 30,000; Full time

Benefits offered by this job

Paid internship

Job summary

A leading research institution is offering a project-based internship in Austin, TX, focusing on building innovative neural mapping for hand intent translation. The role encompasses developing deep models and optimizing inference engines while ensuring low-latency performance. Candidates should demonstrate proficiency in PyTorch, ONNX optimization, and have a passion for biological signals and their applications in robotics. This paid position runs through mid-May 2026 on-site in Austin.

Qualifications

• Advanced proficiency in PyTorch with hands‑on experience in time‑series or biological signal modeling.
• Expertise in ONNX optimization and edge‑device model deployment.
• Familiarity with biomechanical constraints and skeletal rigging logic.
• Fluent in English, written and spoken.

Responsibilities

• Build the sEMG-to-Kinematic labeling pipeline using MediaPipe.
• Develop and train deep temporal models for hand skeletal regression.
• Deploy and optimize inference engines to meet latency requirements.

Skills

PyTorch

ONNX optimization

sEMG signal modeling

TensorRT

QAT

biomechanical constraints

skeletal rigging logic

English fluency

Job description

Wetour Robotics | Austin, TX (Onsite) | Project-Based | Paid | Apr-May 2026

Intro

Wetour Robotics builds the OS layer for Physical AI. We are launching Orchestra, our first public product, at an industry event in Austin in mid-May 2026. This role owns the neural map that translates raw muscle signals into real-time hand intent. What you build will run live on stage at the launch.

What You'll Do

• Build the sEMG-to-Kinematic labeling pipeline using MediaPipe for ground-truth skeletal generation.
• Develop and train deep temporal models (Transformer/GRU/CNN) for continuous 21-node hand skeletal regression from non-linear muscle potentials.
• Deploy and optimize inference engines using TensorRT and Quantization-Aware Training (QAT) to hit under 20ms end-to-end latency on Jetson hardware.

What We're Looking For

• Advanced proficiency in PyTorch with hands‑on experience in time‑series or biological signal modeling.
• Expertise in ONNX optimization and edge‑device model deployment.
• Familiarity with biomechanical constraints and skeletal rigging logic.
• Ability to view sEMG signals as a high‑fidelity map of human intent, not noise to be filtered out.
• Obsessed with latency — 20ms is the ceiling, not the target.
• Comfortable at the intersection of biological signals and skeletal rigging logic without needing a manual.
• Experience deploying TensorRT and QAT by default, not after everything else fails.
• Passion evident through a personal folder of hand‑tracking experiments.
• Fluent in English, written and spoken.

Role Details

Project-based internship, now through mid‑May 2026. Preferred In‑person in Austin, TX. Paid.