Build and productionize AI applications integrating LLMs and vision models into an edge-to-cloud robotics platform. Fine-tune and quantize models, deploy to NVIDIA Orin/Jetson and cloud GPUs, integrate Vector DBs/RAG/agent frameworks, and create data pipelines to collect and version sensor data for retraining.
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.
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.
The base pay range for this role is $175,000 – $210,000 per year.
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