Founding Robotics Software Engineer

About Destro AI

Destro AI is building the intelligence layer for robotics — the system that coordinates robots, humans, and workflows across complex real-world operations.

Founded by former leaders from HAI Robotics, Dexterity, and Matic, Destro is built on a simple thesis: robotics has solved movement, but not decision-making.

At the core of Destro is MothershipOS — a centralized brain that coordinates robots, humans, and workflows in real time. Unlike traditional systems that rely on local intelligence on each robot, Destro brings global intelligence across the entire operation.

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

The Role

A foundational engineering hire. You will build the core software that powers our platform — ML models, distributed services, and the systems that run them in production — then go to customer sites to make sure it actually works.

This is a hybrid of deep software work and forward-deployed engineering. Code most days, on-site when it matters.

What You’ll Do

* Build core services — APIs, data infrastructure, and the abstractions the product is built on

* Train, fine-tune, and deploy ML models (PyTorch, transformers, foundation models, RL) into production

* Own correctness, latency, and reliability for systems running 24/7 in customer environments

* Deploy on-site at customer facilities: integrate, test end-to-end, and debug across software, APIs, and networking

* Drive systems from pilot to production against real KPIs — throughput, uptime, accuracy

* Work directly with the founder and customers to turn field realities into product

Who You Are

•      3–7 years in software engineering, ML, or robotics

•      Strong CS fundamentals and production experience training and deploying ML models

•      Built non-trivial systems that other engineers depend on

•      Comfortable on a customer site debugging live systems under pressure

•      High ownership, bias for shipping

Tech Stack

•      Python and C++ (production experience in at least one)

•      PyTorch (or TensorFlow / JAX) and modern ML tooling — training, evaluation, deployment

•      Foundation models, transformers, RL, or computer vision

•      Distributed systems — gRPC, Kafka, Redis, event-driven architectures

•      Linux, Docker / Kubernetes, AWS / GCP / Azure, GPU infrastructure

•      Bonus: simulation, robotics middleware, real-time systems, or open-source contributions

Why This Role

* Foundational equity and ownership over the core product

* Direct work with the founder — no layers, no politics

* Real production deployments with enterprise operators from day one

* Hard problems at the intersection of ML, systems, and the physical world