AI Engineer (Adaptive Feedback & Assessment/ Learner Graph & Recommendations)

AI Engineer #1 – Adaptive Feedback & Assessment

Mission: Own the brains behind Genie’s instant feedback: deliver marking, commenting, and note-summary models that feel human, hit ≥ 95 % accuracy, and answer in

Key Responsibilities:

● Train & fine-tune NLP / vision models for Math & Science auto-marking, AI commenting and class-note summarisation.

● Build retrieval-augmented explanation pipelines so every hint cites workbook lines or teacher notes.

● Ship fast: instrument latency, automate regression tests on 2 k+ sample sets, and roll out safely via feature flags.

● Work with Curriculum to label edge-case data and measure real-world accuracy weekly.

● Add guard-rails — bias checks, “double-check with a teacher” fallback when confidence dips.

Success Looks Like:

● 95 %+ exact-match score on marking benchmark; p95 latency

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● Models & infra ready for staged roll-outs (staff → beta → all) with zero downtime.

AI Engineer #2 – Learner Graph & Recommendations

Mission: Design the intelligence that makes Genie a “smart-cool friend” — a mastery graph, progress road-maps, and streak-goal engine that boost weekly practice by +20 %.

Key Responsibilities:

● Model concept mastery and temporal learning gaps in a graph database (Neo4j / Amazon Neptune).

● Build recommender services that output next-best-question sets and personalised streak targets.

● Collaborate with Product & Design to expose explainer APIs (“Why Genie recommended this”).

● Own real-time data pipelines (Kinesis / Kafka) feeding the graph with practice logs and class interactions.

● A/B test algorithms; lift practice minutes by ≥ 15 %, track WAU/MAU and retention impact.

Success Looks Like:

● Recommender boosts average practice minutes +15 % within 7 days of exposure.

● Gen-2 streak engine live by 15 Oct 2025 with

● Learner Graph uptime 99.9 %,

AI Engineer #1 – Adaptive Feedback & Assessment

Must-Haves

● 3 + yrs building NLP or multimodal ML systems in Python (PyTorch / TensorFlow).

● Hands-on with transformers, LoRA / PEFT, RAG, and prompt-engineering best practice.

● Solid software chops: FastAPI or equivalent, CI/CD, Docker/K8s, AWS or GCP.

● Obsessed with measurement and rapid experiment cycles.

Nice-to-Haves

● Education-tech, grading, or document-understanding experience.

● Familiarity with Vision-Language models for diagram questions.

● Singapore work authorisation (role is SG-hybrid; remote SEA considered for the right fit).

AI Engineer #2 – Learner Graph & Recommendations

Must-Haves

● 3 + yrs in recommender systems, graph or sequence modelling, strong SQL + Python.

● Production experience with graph databases and AWS data tooling (Glue, Redshift, Kinesis).

● Proven A/B experimentation mindset; fluent in statistical evaluation.

● Ability to partner with front-end teams on SDK contracts and telemetry.

Nice-to-Haves

● Reinforcement-learning or bandits in user-engagement contexts.

● Prior work on gamification or ed-tech analytics.

● Familiarity with TypeScript/React for light SDK contributions.

Why Geniebook?

● Mission — help Every1 reach A1 using cutting-edge AI.

● Scale — millions of answers, live classes, and chat sessions to learn from.

● Autonomy — own a pillar of the 2025_2027 roadmap and see it in customers’ hands fast.

● Culture — small, execution-obsessed team where shipping beats slide-ware.