The Role
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.
Key Responsibilities
● 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.