Job Title

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 hello@ 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 howAvailability to start within 1–2 weeks and timezone(Optional) A brief note on grams estimation from depth vs. monocular on plated dishesMission (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 fromuser corrections(renames, grams/macros edits) and can retrain/evaluate safely.What you will build (end-to-end scope)Public APIsPOST /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 coachReplyFood 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 awareSafety: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|heuristicNutrition & ingredientsMap labels →canonical taxonomy(≤400 dishes)Queryour nutrition DBor external sources (e.g., FDC) to assembleingredients + per-ingredient macros , scale by grams, compute meal totalsRetraining loop (feedback → model)Capture user edits & low-margin/OOD crops → store to ClickHouse/S3Scripts & jobs to rebuild datasets, fine-tune,evaluate with metric gates , and publish new artifacts safelyOps & safetyCI evaluator (Top-1/Top-5, OOD FP rate, Portion MAPE, latency SLOs) thatblocks regressionsObservability: structured logs, per-stage ms, model/taxonomy versionsPrivacy: consent gate, retention/“delete my images” flow60-Day milestone plan (acceptance-driven) Week 1–2 (Foundation & API) Stand up GPU FastAPI /infer-v2 + Node /api/coach/photoReturn stubbed payload matching contract; basic telemetry; dockerizedDemo:curl upload → JSON schema exactly matches app contractWeek 3–4 (Models & Portions) YOLO gate+seg (export ONNX); CLIP/SigLIP naming with temperature scalingDepth-aware grams (device depth) + mono fallback; density via RedisDemo:multi-cuisine sample set returns names + grams within sanity boundsWeek 5 (Nutrition & Safety) Taxonomy (≤400) + nutrition mapping (our DB / FDC)OOD abstain with suggestions; ingredients + per-ingredient macros scaled by gramsDemo:App-ready payload { name, grams, confidence, tags, ingredients, macros } per item; meal totals & remainingDailyWeek 6–8 (Retraining + CI gates + Canary) Feedback capture from user edits; dataset rebuild scripts; fine-tune pathEvaluator + CI gates (json report) and shadow/canary rollout togglesPrivacy & retention wired; runbook + handover docsFinal Demo (Day 60):end-to-end flow on staging GPU; retrain on a small corrected set; CI passes; canary toggle readySuccess metrics (set at kickoff; used by CI gate)Quality:Top-1 on core ≥ target; OOD FP ≤ target; Portion MAPE ≤ target on depth imagesLatency:p50 ≤350 ms , p95 ≤800 mson our staging GPUReliability:CI gate prevents regressions; logs/metrics complete; consent & retention enforcedMinimum qualificationsShippedcomputer-vision systemsto production (beyond notebooks)YOLO detect/seg training or fine-tuning; export toONNX/TensorRTand debug opsets/dynamic shapesCLIP/SigLIP or ViT classifier work (fine-tune +temperature scaling ); OOD thresholdingDepth pipelines (device + monocular), geometric reasoning (plane fitting, coverage)Production APIs (FastAPI/Node), Redis/ClickHouse (or similar), Docker, GitHub ActionsObs/ops: structured logging, latency profiling, privacy/retention patterns Nice-to-haves Triton Inference Server, FAISS/ANN, K8s/Helm, W&B/MLflowNutrition data integration (FDC or equivalent), taxonomy designTech 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 feedbackClear API contract and benchmark packs for CI gatingHow to apply Emailhello@with subject“60-Day CV & Backend Build — WowNom”and include: A shipped CV project (repo/demo) + onelatencyand oneaccuracynumber you achieved and howAvailability to start within 1–2 weeks and timezone(Optional) A brief note on grams estimation from depth vs. monocular on plated dishes