About Destro

Destro is building the intelligence layer for warehouse robotics. We develop the software that enables robots to perceive their environment, make decisions, and manipulate objects across complex real-world operations.

Founded by experienced leaders from the warehouse automation industry, DestroAI is built on a core Our platform gives robotic systems the ability to handle any object in any warehouse, without per-product programming.

We recently closed our Seed round at one of the highest valuations for a robotics company at this stage and are already deploying systems with large enterprise operators.

Role Overview

You will own three connected technical domains that form a single end-to-end loop: perception (making the robot understand its environment), teleoperation (operating the robot to generate high-quality training data), and robot learning (turning that data into models that generalize to new objects and tasks). Perceive, demonstrate, learn, repeat.

This is not a support role or a ticket queue. You own a defined technical domain, set your own technical approach, and are expected to operate with a high degree of independence.

This role is ideal for someone who is equally comfortable:

• Building perception pipelines from depth cameras and point clouds
• Operating robot arms via teleoperation to collect demonstration data
• Fine-tuning large foundation models on custom robotic task data
• Working across simulation and real hardware as primary development environments
• Shipping systems that have to work reliably in physical warehouse settings

What You'll Do

01 Perception

• Build and own the 3D scene understanding pipeline: RGB-D sensor fusion, point cloud processing, and object segmentation.
• Integrate and tune 6-DOF object pose estimation for warehouse items including cases, pallets, and mixed-SKU bins.
• Implement zero-shot object identification using vision-language model APIs.
• Own the sim-to-real transfer gap for perception: calibrate real depth sensors to match simulated camera models.

02 Teleoperation & Data Collection

• Integrate a teleoperation interface (VR, joystick, or leader-follower setup) with the physical robot; build a monitoring stack covering robot state, camera feeds, and force/torque readings.
• Run teleoperation sessions across core manipulation use cases: picking, depalletizing, and palletizing.
• Build the raw recording-to-training dataset pipeline: timestep alignment, action labeling, quality filtering, and version control.

03 Robot Learning & Control

• Integrate Vision-Language-Action (VLA) model APIs as the task execution backbone, enabling the robot to interpret and act on natural-language task instructions.
• Own the model fine-tuning pipeline: synthetic data generation in simulation, dataset formatting, parameter-efficient fine-tuning on GPU hardware, and evaluation against held-out benchmarks.
• Build and maintain the active learning loop: flag low-confidence episodes, re-demonstrate, and fold corrections back into training.
• Maintain rigorous experiment tracking: every training run logged, every model version registered, every dataset versioned.

Who You Are

• 3–5 years of hands-on experience across robotics, computer vision, or machine learning — preferably involving physical robot systems, not purely academic projects.
• Comfortable operating across perception, teleoperation, and robot learning; you don’t need to be an expert in all three, but you should be excited to work in each.
• Strong software engineering foundation with the ability to debug complex, real-world problems quickly.
• Hands-on model fine-tuning experience — parameter-efficient methods (LoRA, QLoRA) or full fine-tunes on GPU hardware.

Technical Skills Required

• Python and C++ (or similar systems languages)
• 3D point cloud processing and depth camera pipelines
• Deep learning model fine-tuning with modern ML frameworks
• Experiment tracking and dataset management (e.g., W&B, MLflow, DVC, or equivalent)
• Physics simulation for robotics (e.g., MuJoCo, Isaac Sim, PyBullet, or similar)
• Sim-to-real deployment on physical robot hardware
• Teleoperation experience: operating robot arms via VR, leader-follower, joystick, or similar interface
• Linux, Git, CI/CD pipelines

Strongly Preferred

• Experience with zero-shot pose estimation or segmentation models
• Vision-Language-Action models or robot manipulation policy training
• Synthetic data generation and domain randomization in simulation
• Robot demonstration dataset formats for imitation learning
• Grasp planning (learned or geometric approaches)
• Experience with any robot arm SDK
• Warehouse, logistics, or physical automation environment exposure