AI Engineer

AI Engineer

Full-time

Remote in US

About the Role

As an AI Engineer at Causally, you'll build the AI-native features that make Manta more than a system of record — think embedded assistants that help teachers interpret DIBELS and other assessment data, automated flagging for MTSS/RTI interventions, and natural-language interfaces over student data that respect the sensitivity and regulatory weight of K–12 data. You'll work at the intersection of applied LLM engineering, data pipelines, and real classroom workflows, with fast feedback loops from actual education partners.

This is a hands-on, build-things role at a startup where AI engineering directly shapes the product roadmap — not a research role removed from shipping.

What You'll Do

·         Design, build, and ship AI features inside Manta — assistants, automated report generation, natural-language query interfaces, and recommendation/flagging systems

·         Go beyond incremental features on the existing product: prototype and stand up new, adjacent products in the SIS space — e.g., predictive early-warning systems, new assessment or analytics modules, or entirely new data products that extend what a "system of informed action" can do beyond Manta's current feature set

·         Apply data science skills (statistical modeling, predictive analytics, cohort/trend analysis) to surface new product opportunities from K–12 data, not just to support existing workflows. This includes exploratory work to identify what a new SIS-adjacent product could look like before it's scoped as a feature

·         Work with structured K–12 data (assessment scores, attendance, gradebook, behavior data) to build reliable, explainable AI outputs educators can trust and act on

·         Own prompt design, retrieval architecture, and evaluation pipelines for LLM-powered features

·         Partner closely with product and design partners to translate real needs into working features and to identify unmet needs that could become net-new products rather than Manta feature additions

·         Build and maintain the infrastructure for reliable AI behavior in production: evals, guardrails, monitoring, and cost/latency tradeoffs

·         Contribute to technical decisions on model selection, fine-tuning vs. prompting, and build-vs-buy for AI infrastructure

·         Uphold a high bar for data privacy and responsible AI use given the sensitivity of student data (FERPA and related considerations)

Role requirements

Requirements

·         1 -​ 6 years of experience in AI/full-​stack engineering,​ with hands-​on LLM experience (RAG,​ prompting,​ agents)

·         Strong Python and production software engineering fundamentals

·         Has shipped LLM-​based features to production (RAG,​ prompting,​ agentic workflows,​ and/or fine-​tuning)

·         Genuine interest in education,​ mission-​driven work,​ or K-​12 systems

·         Full-​stack software engineering background

·         Prior work in a highly regulated industry (ed-​tech,​ healthcare,​ finance)

Education

BS or MS in Computer Science or related engineering field from a top school if more junior

Data science skills:​ statistical modeling or predictive analytics

Soft skills

Highly curious and motivated;​ signs of genuine interest in the craft (e.​g.​,​ hackathons,​ Github presencem side projects)