Sr. Applied AI/ML Scientist
Tasks
- Architect production agentic workflows
- Build LLM observability tracing cost latency and quality metrics
- Define structured prompting and output schemas
- Deliver LLM powered content enrichment
- Evaluate emerging models and techniques
- Implement hybrid search and reranking
- Implement safety hallucination reduction and factual grounding
- Improve embedding and semantic search infrastructure
- Lead technical design reviews and code review
- Lead tool augmented LLM system design
- Monitor drift and regression detection
- Optimize retrieval relevance in agentic architectures
- Partner with product and UX to ship features
- Set LLM evaluation benchmarks
Perks/Benefits
- Dental insurance
- Disability insurance
- Life insurance
- Mental health support
- Paid parental leave
- Paid time off
- Pension match
- Private health insurance
- Work from home support
Skills/Tech-stack
Anthropic API | Artificial Intelligence | Automated testing | ChromaDB | Chunking | Cost Optimization | Datadog | Embeddings | Factual Grounding | Fine Tuning | Function Calling | Hallucination reduction | Human-in-the-loop | Hybrid search | LLM Evaluation | LLMOps | Langchain | Langfuse | Langgraph | Language Models | Large Language Models | Latency optimization | Llamaindex | MCP | Machine Learning | Model Context Protocol | Model Fine-tuning | Observability | OpenAI API | Pinecone | Prompt engineering | Python | Relevance optimization | Reranking | Retrieval-Augmented Generation | Safety | Semantic Search | Similarity Search | Structured Output | The Loop | Tool use | Tracing | Transformers | Vector Databases | Weaviate
Education
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