ML/AI Engineer, Applied AI
San Francisco, CA; New York, New York; Onsite
A USD 180K-275K Senior-level Full Time
Tasks
- Balance AI quality latency reliability and cost
- Build LLM as judge and human review workflows
- Build production AI systems
- Build retry fallback and failure handling mechanisms
- Build structured extraction and classification workflows
- Create evaluation datasets and golden sets
- Design AI evaluation and quality measurement systems
- Develop Python services and data pipelines
- Develop retrieval and context construction pipelines
- Implement LLM powered applications with hosted models
- Implement observability and monitoring for production AI systems
- Improve AI reliability debugging and troubleshooting
- Optimize prompts schemas and context selection
- Orchestrate multi step AI workflows and tool calling
- Transform enterprise data into structured outputs
Perks/Benefits
- Direct collaboration with founders
- High ownership
- In-office Flexibility
- In-office collaboration
- Remote work
Skills/Tech-stack
APIs | Agent Orchestration | Chunking | Cloud infrastructure | Context Assembly | Docker | Embeddings | Evaluation datasets | Failure handling | GCP | Human review | Hybrid search | LLM Evaluation | LLM-as-judge | Language Models | Large Language Models | Monitoring | Observability | PostgreSQL | Prompt engineering | Python | RAG | Ranking | Redis | Regression testing | Reranking | Retries | Retrieval-Augmented Generation | Structured Output | Structured extraction | Tool-Calling | Vector Search
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
Roles
AI Engineer | Applied AI Engineer | Engineer | ML & AI Engineer
Regions
Countries
States
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