Applied AI Software Engineer

At Kerrigan Robotics, we aren’t just adding more "iron" to the factory floor; we’re building the intelligence that brings it all to life. As an AI-powered, robot-agnostic orchestration platform, we bridge the gap between high-level software and complex physical hardware, harmonizing everything from humanoids and drones to legacy ERP and WMS systems. Our team’s pedigree is rooted in scaling the impossible, having first proven our mettle at Tesla by scaling $5B worth of equipment and thousands of robots globally before achieving seamless multi-branded integration for Rivian’s fleet and lineside controls.  While the rest of the industry focuses on building individual robots, we are operationalizing heterogeneous automation at scale slashing production ramp-up times by 70% and making enterprise level expansion feel effortless. If you’re ready to turn fragmented data into harmonious communication and help make the future of manufacturing simpler, more reliable, and truly unified, your next challenge starts here.

As an Applied AI Software Engineer, you sit at the intersection of Machine Learning and

robust Software Engineering. Your goal is to move AI out of notebooks and into production. You

will be responsible for building the applications that leverage modern LLMs and Vision models,

fine-tuning them for specific industrial use cases, and ensuring they run efficiently on our edge-

to-cloud platform.

AI Application Building: Develop software that integrates modern AI tooling (Vector DBs,

RAG, Agentic frameworks) into the Kerrigan ecosystem.

Model Optimization: Perform fine-tuning and quantization of open-source models to

optimize for specific robotics tasks.

Deployment: Work with the Platform team to deploy models on NVIDIA Orin/Jetson or

cloud GPUs using TorchServe, ONNX, or TensorRT.

Data Pipelines: Build the infrastructure to collect, clean, and version-control data from

physical sensors for model retraining.

Requirements

Experience: 4+ years of software engineering, with at least 2 years focused on AI/ML

implementation.

Modern AI Stack: Hands-on experience with frameworks like PyTorch, HuggingFace, and

LangChain/LlamaIndex.

Fine-Tuning: Proven experience fine-tuning LLMs or Vision models (LoRA, QLoRA) for

niche domains.

Software Rigor: Strong coding skills in Python and Go.

Physical AI (Plus): Experience with Computer Vision (OpenCV), point cloud processing,

or deploying AI on physical hardware/edge devices is a significant advantage.

What This Role Is Not

This is not a pure Research Scientist role; we value shipping functional products over

publishing papers.

This is not a data labeling role; you are building the systems that use the data.