Data Scientist

Archetype AI is developing an innovative AI platform that integrates AI into the physical world. They are seeking an AI Solutions Specialist to join their Go-To-Market Sales team, where the role involves rapidly transforming customer ideas into practical AI solutions through data preparation and prompt engineering.

Responsibilities:

• Review and assess incoming customer assets (video, sensor streams data, use cases, requirements) for feasibility with Newton
• Prepare datasets in Python for evaluation and fine-tuning, including ingestion, cleaning, structuring, and steps such as imputation, filtering, normalization, or other basic signal processing for model readiness
• Support labeling and annotation workflows from dataset creation through evaluation
• Use signal visualization and basic statistical analysis to assess and validate data preparation steps
• Collaborate with engineering to investigate unexpected model performance and identify opportunities for improvement, noting that data preparation is often iterative (data prep → testing → refine prep → repeat)
• Design, test, and refine prompts — including natural language and multimodal examples (e.g., n-shot examples in the Machine State Lens) — for Newton’s lenses (physical agents) based on specific customer needs
• Configure and adjust lens parameters to maximize performance on quick evaluations and proof-of-concept runs
• Maintain a library of reusable prompt templates and configuration presets for common scenarios
• Work closely with Solutions Architects, Solutions Engineers, and Sales Representatives to support the GTM team
• Support Solutions throughout the entire customer engagement lifecycle — from top-of-funnel discovery through solution development, deployment, and follow-on expansion
• Summarize evaluation findings for the Solutions teams, enabling faster follow-up with customers
• Occasionally support demos or training sessions on prompt building and evaluation best practices
• Take ownership of assigned evaluation projects, driving them from raw data to testable results without waiting for step-by-step direction