The positionWe're looking for an Applied AI Engineer to take our growing collection of foundation models and ML components from manually run, sometimes locally trained workflows to fully automated, production-grade MLOps pipelines: deployed reliably on robots in nursing facilities. We need someone who knows the model landscape cold, treats evaluation as a first-class engineering problem, and has strong opinions about when to prompt, RAG, fine-tune, swap, or buy.
You'll work across cloud and edge deployments, and some of the systems you'll touch are on a SaMD pathway, so you'll need to be comfortable shipping under regulatory constraints.
What you'll do- Integrate foundation models and ML components (VLMs, LLMs, ASR/TTS, detection/segmentation, embeddings) into our production pipelines, using both open-weight models and third-party APIs
- Build RAG and agent-style orchestration for clinical reporting and conversational interfaces
- Ship real-time streaming pipelines (voice agents) alongside batch and request-response workloads
- Build evaluation harnesses that catch regressions across model swaps and measure performance against clinical-grade accuracy targets
- Fine-tune and retrain models (LoRA, PEFT, supervised fine-tuning) using data collected from our deployed fleet
- Deploy across our inference surfaces: third-party APIs, self-hosted, and on-robot edge
- Build the data flywheel: pipelines that collect, label, version, and feed production data back into model improvement
- Partner with the algorithms team (signal processing, computer vision) on integration with their lower-level pipelines
What we're looking for- BS in Computer Science, Engineering, or a related field, or equivalent hands-on experience
- 4+ years shipping ML/AI systems in production outside of academic settings
- Strong working knowledge of the modern foundation model landscape (open-weight LLMs and VLMs, common detection/segmentation backbones, embedding models)
- Hands-on experience with PEFT/LoRA and supervised fine-tuning
- Strong Python; comfortable with the deployment toolchain (ONNX, quantization, at least one inference runtime-TensorRT, vLLM, llama.cpp, etc.)
- Experience with a cloud ML training/MLOps platform (GCP Vertex AI, AWS SageMaker, Azure ML, or equivalent)
- Ability to work independently, solve complex problems, and drive projects to completion
Bonus points- Edge ML deployment (Jetson, ARM, mobile NPUs)
- Real-time voice AI pipelines (STT, TTS, streaming LLM)
- Production RAG systems beyond toy implementations
- Medical devices, SaMD, or other regulated ML environments
- MLOps tooling (Weights & Biases, MLflow, DVC, etc.)
- Active learning or human-in-the-loop labeling workflows
- C++ for integrating with our computer vision pipeline
What we offer- Real impact: your code provides care for patients today
- High autonomy and technical ownership-you'll define how we operate AI in production
- Work at the intersection of cutting-edge AI, edge computing, and healthcare
- A talented, excellent, diverse and international team
- Equity participation in the company's future
- Cutting-edge stack: embedded AI, robotics, LLMs, multimodal sensing
- Transparent, mission-driven culture focused on continuous learning
- Competitive salary and equity
