01Key Responsibilities
- Model Orchestration: Design and manage complex workflows across both open-source and closed-source LLMs.
- RAG & Knowledge Graphs: Build robust pipelines for data cleaning and transformation for RAG; research and implement SOTA approaches like Graph RAG.
- Evaluation & Optimization: Develop and run multi-rubric evals for text classification, sentiment analysis, and STT; implement inference-time reasoning to boost model accuracy.
- Data Mastery: Clean, prune, and collect high-quality datasets to minimize hallucination rates and optimize recall and performance.
- Scalable Infrastructure: Take ownership of moving AI features from prototype to a scalable, robust production environment.
Required Qualifications:
- Technical Pedigree: 3+ years of experience with Machine Learning, NLP, and Prompt Engineering.
- Proven Impact: Experience building systems from 0 to 1 or 1 to 10 (typically in early or growth-stage startups.
- Hands-on AI Experience: Mandatory experience in optimizing prompts, building RAG pipelines, and running rigorous model evals.
- Action-Oriented: A track record of thoughtful work evidenced by side projects, open-source contributions, or technical blogs rather than just a CV.
- Cultural Alignment: Deep resonance with our values: "Doers over Talkers," 360-degree feedback, and human-centric design.
Our Core Values:
- Product Ownership: Everyone, including engineers, engages in customer outreach and support to identify and build the right features.
- Flat Hierarchy: No one is too senior for any taskour CTO codes daily, and everyone handles customer support on a rotation.
- Empathy First: We build for humans. Kindness and empathy are core requirements for every team member.
