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Software Backend Engineer (AI,LLM)(2-Month Contract)

WownomDelhi, IN

6 hours ago

Job description

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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Software Engineer Backend • Delhi, IN

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