Backend Engineer (2-Month Contract)

Computer Vision & Backend Engineer (60-Day Build) Company: WowNom Type: Fixed-term contract (60 days, full-time) — extension possible Location: Remote (Singapore Time, APAC-friendly hours) How to apply Email with subject “60-Day CV & Backend Build — WowNom” and include: A shipped CV project (repo/demo) + one latency and one accuracy number you achieved and how Availability to start within 1–2 weeks and timezone (Optional) A brief note on grams estimation from depth vs. monocular on plated dishes Mission (60 days) Deliver a production-ready photo recognition system that powers a calorie-counting app end-to-end: Upload → Analyze → Nutrition: From a food photo, return { name, grams, confidence, tags, ingredients, macros } per item, with meal totals and remaining daily targets. Retraining option: Design and ship the infrastructure that learns from user corrections (renames, grams/macros edits) and can retrain/evaluate safely. What you will build (end-to-end scope) Public APIs POST /api/vision/upload (multipart JPEG/PNG/WebP) → { name, grams, confidence, tags }() POST /api/coach/photo → persist image, call vision, run lookupFood, return items, meal totals, remaining Daily, and coachReply Food analysis (multi-cuisine) Gate + Instances: YOLOv8/11 detect (food vs distractors) → YOLO-seg (retina masks) Naming: SigLIP/CLIP (or compact ViT) on mask crops, synonyms/taxonomy aware Safety: OOD detector + low-confidence suggestions; safe abstain (no hallucinations) Portioning (grams) Device-depth first (if present), monocular fallback (MiDaS/ZoeDepth), tabletop plane-fit, coverage %, density lookup (Redis), portion_source=device|mono|heuristic Nutrition & ingredients Map labels → canonical taxonomy (≤400 dishes) Query our nutrition DB or external sources (e.g., FDC) to assemble ingredients + per-ingredient macros , scale by grams, compute meal totals Retraining loop (feedback → model) Capture user edits & low-margin/OOD crops → store to ClickHouse/S3 Scripts & jobs to rebuild datasets, fine-tune, evaluate with metric gates , and publish new artifacts safely Ops & safety CI evaluator (Top-1/Top-5, OOD FP rate, Portion MAPE, latency SLOs) that blocks regressions Observability: structured logs, per-stage ms, model/taxonomy versions Privacy: consent gate, retention/“delete my images” flow 60-Day milestone plan (acceptance-driven) Week 1–2 (Foundation & API) Stand up GPU FastAPI /infer-v2 + Node /api/coach/photo Return stubbed payload matching contract; basic telemetry; dockerized Demo: curl upload → JSON schema exactly matches app contract Week 3–4 (Models & Portions) YOLO gate+seg (export ONNX); CLIP/SigLIP naming with temperature scaling Depth-aware grams (device depth) + mono fallback; density via Redis Demo: multi-cuisine sample set returns names + grams within sanity bounds Week 5 (Nutrition & Safety) Taxonomy (≤400) + nutrition mapping (our DB / FDC) OOD abstain with suggestions; ingredients + per-ingredient macros scaled by grams Demo: App-ready payload { name, grams, confidence, tags, ingredients, macros } per item; meal totals & remainingDaily Week 6–8 (Retraining + CI gates + Canary) Feedback capture from user edits; dataset rebuild scripts; fine-tune path Evaluator + CI gates (json report) and shadow/canary rollout toggles Privacy & retention wired; runbook + handover docs Final Demo (Day 60): end-to-end flow on staging GPU; retrain on a small corrected set; CI passes; canary toggle ready Success metrics (set at kickoff; used by CI gate) Quality: Top-1 on core ≥ target; OOD FP ≤ target; Portion MAPE ≤ target on depth images Latency: p50 ≤ 350 ms , p95 ≤ 800 ms on our staging GPU Reliability: CI gate prevents regressions; logs/metrics complete; consent & retention enforced Minimum qualifications Shipped computer-vision systems to production (beyond notebooks) YOLO detect/seg training or fine-tuning; export to ONNX/TensorRT and debug opsets/dynamic shapes CLIP/SigLIP or ViT classifier work (fine-tune + temperature scaling ); OOD thresholding Depth pipelines (device + monocular), geometric reasoning (plane fitting, coverage) Production APIs (FastAPI/Node), Redis/ClickHouse (or similar), Docker, GitHub Actions Obs/ops: structured logging, latency profiling, privacy/retention patterns Nice-to-haves Triton Inference Server, FAISS/ANN, K8s/Helm, W&B/MLflow Nutrition data integration (FDC or equivalent), taxonomy design Tech you’ll touch PyTorch, Ultralytics YOLOv8/11, SAM/SAM2, SigLIP/CLIP, MiDaS/ZoeDepth, ONNX Runtime (CUDA EP), TensorRT (nice), FastAPI, Node/Express, Redis, ClickHouse, Docker, GitHub Actions. What we provide GPU access (cloud, H100/A10/T4), seed datasets & taxonomy draft, staging infra, and rapid product feedback Clear API contract and benchmark packs for CI gating How to apply Email with subject “60-Day CV & Backend Build — WowNom” and include: A shipped CV project (repo/demo) + one latency and one accuracy number you achieved and how Availability to start within 1–2 weeks and timezone (Optional) A brief note on grams estimation from depth vs. monocular on plated dishes

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