Design software systems spanning multiple services to run Physical AI processes across heterogeneous architectures (CPU, GPU, NPU, FPGA).

Build, optimize, and test software pipelines for deploying neural networks, large language models (LLMs), vision transformers, VLMs, and VLAs directly to edge devices.

Deploy and accelerate deterministic robotics algorithms, ensuring they meet strict real-time execution constraints alongside machine learning workloads.

Set code quality standards for the team and design test strategies for entire features, ensuring that your definition of "done" means it works on the floor, not just in CI.

Drive alignment across cross-functional teams by proactively coordinating handoffs across roles (e.g., firmware, software, deployment) instead of waiting to be looped in.

Plan across projects with clarity and confidence, breaking large initiatives into independently shippable increments and flagging schedule risk early.

Push back on requirements that seem technically infeasible and run scoped spikes to de-risk hard problems rather than just flagging concerns.

What We Are Looking For

Proven experience working with state-of-the-art algorithms used in autonomous systems or advanced robotics.

Proven experience deploying complex algorithms onto embedded and edge devices (specifically beyond standard laptops and desktop PCs).

Deep understanding of hardware execution models (e.g., memory hierarchies, compute pipelines) to write software that maximally exploits the underlying compute and anticipates bottlenecks.

Experience accelerating workloads on CPUs, GPUs (e.g., CUDA, TensorRT, ROCm) or FPGAs.

Senior AI Compute Engineer

Responsibilities

Qualification

Required

Benefits

Company

Trener Robotics

More roles at Trener Robotics