Applied AI Engineer
Tasks
- Build Retrieval Augmented Generation systems
- Collaborate with cross-functional partners
- Curate clean and maintain training datasets
- Design evaluation frameworks and test harnesses
- Develop and optimize model serving infrastructure
- Fine-tune language models
- Ground model outputs in external knowledge
- Implement post training and alignment methods
- Integrate AI capabilities into applications
- Write technical documentation for models and datasets
Perks/Benefits
- 401k retirement plan
- Bike storage
- Commuter benefits
- Dependent care FSA
- Desk setup stipend
- Disability coverage
- Fitness Center
- Flexible PTO
- HSA & FSA options
- Life and AD D Insurance
- Medical, dental, and vision insurance
- On-site parking
- Paid Holidays
- Paid parental leave
Skills/Tech-stack
AWS | AgentOps | Azure | ChromaDB | Continued Pretraining | CrewAI | Dataset Preprocessing | Deep learning | Direct Preference Optimization | Distributed Training | Docker | Evaluation metrics | Experiment tracking | Fine Tuning | GCP | Graph Databases | Graph retrieval | Human Feedback | JAX | Kubernetes | LLM Agents | LLMOps | Langchain | Langgraph | Language Model | Language model fine-tuning | Learning from Human Feedback | MLOps | Milvus | Model Alignment | Model Fine-tuning | Monitoring | ONNX | ONNX Runtime | PGVector | Pinecone | Policy Optimization | Post-training | Preference optimization | Prompt engineering | Proximal Policy Optimization | PyTorch | PydanticAI | Python | Quantization | Reinforcement Learning | Reinforcement Learning from Human Feedback | Relational databases | Retrieval-Augmented Generation | Supervised Fine Tuning | TensorFlow | TensorRT | VLLM | Vector Databases | Weaviate
Education
N/A
Regions
Countries
States
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