Backend Engineer

2 weeks ago


Tirupati, India Wownom Full time

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@wownom.com 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 hello@wownom.com 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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