Applied AI Engineer
Tasks
- Build data pipelines for labeling versioning and model improvement
- Build evaluation harnesses for model regression testing
- Build retrieval augmented generation and agent orchestration for clinical reporting
- Deploy models across cloud APIs self hosted and on robot edge
- Fine tune and retrain models with PEFT LoRA
- Integrate foundation models and ML components into production pipelines
- Partner with algorithms team for signal processing and computer vision integration
- Ship real time streaming pipelines for voice agents
Perks/Benefits
- Autonomy
- Continuous learning culture
- Equity participation
- International team
- Mission-driven work
- Technical ownership
Skills/Tech-stack
AWS SageMaker | Agent Orchestration | Azure ML | DVC | Deep learning | Embeddings | Evaluation | Fine Tuning | GCP Vertex | GCP Vertex AI | Inference Runtime | Language Models | Large Language Models | Llama CPP | LoRA | MLOps | MLflow | Machine Learning | ONNX | Object Detection | PEFT | Python | Quantization | Retrieval-Augmented Generation | Segmentation | Speech Recognition | Supervised Fine Tuning | TensorRT | Text to Speech | VLLM | Vertex AI | Vision Language Models | Vision-language | Weights and Biases
Education
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