Senior/Staff Applied AI Engineer

Role Overview

• Design and implement agent execution workflows with structured, validated model outputs
• Build semantic routing and complexity-classification systems across skills, data sources, and models
• Develop classifiers using BERT-style models, embeddings, rules, and ensembles
• Implement multi-turn context handling and state/context-retrieval mechanisms
• Build model-independent interfaces across OpenAI, Google, and open-weight models
• Create synthetic-data generation and validation pipelines
• Fine-tune and evaluate small models and classifiers
• Build grounding, provenance, and factuality/claim-validation mechanisms
• Create automated offline and online evaluation systems
• Analyze production failures and convert them into product improvements
• Optimize quality, latency, and inference cost
• Write production backend code and tests, contributing to architectural decisions through implementation

Requirements

• Substantial experience building production backend systems under ambiguous product requirements
• Hands-on experience implementing LLM-powered applications beyond prompt prototypes (structured outputs, tool execution, agent orchestration)
• Experience with retrieval, classification, ranking, or semantic routing
• Background in classical machine learning and modern language models
• Experience with embeddings and vector-based systems
• Familiarity with supervised fine-tuning, LoRA, adapters, or distillation
• Experience with evaluation design and model error analysis
• Experience with synthetic-data generation
• Strong backend engineering skills in TypeScript, Rust, or another strongly engineered ecosystem
• Practical, disciplined experience working with coding agents (codebase exploration, task decomposition, test generation, large refactors, failure investigation)

Tech Stack

• Rust
• TypeScript