AI Engineering
Computer Vision Engineer
Teaching a machine to see is harder than it sounds and more interesting than it looks. Computer vision engineers shape the systems that read documents, navigate self-driving cars, screen medical images, and answer questions about photos.
The role mixes the math of multi-view geometry with the engineering grind of getting models small and fast enough to run where they're needed — sometimes on a phone, sometimes on a robot. Good work here looks like a pipeline that holds up in real lighting, real motion, and real failure modes.
Students grow into this path by getting hands-on with OpenCV and PyTorch early, then learning the harder craft of optimizing models without quietly destroying their accuracy.
- CodeIntermediateNew
Lane-Change Intent Classifier from Dashcam Video
Use a public driving video dataset (e.g., Argoverse 2 sensor or BDD100K) and curate ~6,000 short clips labeled with the three-class intent. Train a temporal model (e.g., a small…
- Video Understanding
- Temporal Modeling
- Model Evaluation
Visual Intelligence and Visual Reasoning - CodeIntermediateNew
Train an Object Detector for an Autonomous-Forklift Robotics Startup
You receive 12,000 labeled warehouse images (pallets, pedestrians, forklifts) plus a 1,500-image safety-test set heavy on pedestrian edge cases. Train an object detector (YOLOv8…
- Object Detection
- Yolo
- Edge Deployment
Deep Learning for Computer Vision - CodeSeniorNew
Fuse Camera + Audio Cues for an Autonomous-Vehicle Edge Case
You receive a curated dataset of 4,000 short clips (5s each), each with synchronized 8-camera 360-degree video, 4-channel audio, and labels (siren-active emergency vehicle prese…
- Multimodal Perception
- Neural Networks
- Audio Processing
Machine Perception - CodeBeginnerNew
Build a Crop-Disease Classifier for a Smallholder Agritech Startup
You receive a curated 22,000-image cassava-disease dataset across 5 classes (4 diseases + healthy) plus a labeled 1,200-image held-out test set. Train a CNN classifier (start wi…
- Cnn Classification
- Cnn Architectures
- Transfer Learning
Deep Learning for Computer Vision Practice your coursework on real scenarios.
Every challenge is shaped from real-world context — not generic exercises. The work mirrors what your degree prepares you for.
Why Ewance
- CodeBeginnerNew
Calibrate a Multi-Camera Rig for Warehouse Robotics
You will design and prototype a calibration workflow using a printed ChArUco board (a chessboard with embedded ArUco markers). You receive a sample dataset of 200 raw frames per…
- Camera Calibration
- Multi View Geometry
- Opencv
3D Vision and Multi-View Geometry - CodeIntermediateNew
Fuse LiDAR and Camera for an Autonomous Yard Truck
You receive 6 hours of synced LiDAR + 4-camera ring data from yard operations, with 3D bounding-box labels for pedestrians, forklifts, and containers. Build a late-fusion module…
- Sensor Fusion
- Lidar Perception
- Object Detection
Robot Perception and Autonomy - CodeFoundationalNew
Classify Retail Product Photos for an E-Commerce Marketplace
Use a publicly-available product-image dataset (e.g., Fashion-MNIST extended, or a Kaggle e-commerce subset of around 10k images across 12 categories). Fine-tune a small pretrai…
- Cnn Classification
- Transfer Learning
- Pytorch Or Tensorflow
Computer Vision (Undergraduate) - CodeBeginnerNew
Semantic Segmentation for a Solar-Panel Inspection Drone
Use a publicly-available solar-panel dataset (or the PV-Defect-Detection dataset). Fine-tune a small U-Net or SegFormer-tiny on panel/no-panel pixel-level segmentation. Evaluate…
- Semantic Segmentation
- Cnn Classification
- Transfer Learning
Computer Vision (Undergraduate) - Browse challenges
Explore role
Product Manager
Ship product that solves real user problems. Combine user research, prototyping, and stakeholder alignment to turn ambiguous briefs into measurable wins — the role at the centre of modern software teams.
- CodeBeginnerNew
Image-Quality Triage Tool for a Tele-Radiology Network
You receive 10,000 chest-X-ray images with multi-label quality flags (rotation, clipping, motion). Train a small multi-label CNN that outputs a per-flag probability and a single…
- Medical Imaging
- Classification
- Neural Networks
Machine Learning for Imaging and Medical Image Analysis - CodeIntermediateNew
Multi-Sensor Late-Fusion Prototype for an Indoor AGV
Use the public KITTI dataset (or a similar paired LiDAR+RGB dataset) restricted to static-obstacle classes. Implement a late-fusion baseline: a LiDAR-only detector (PointPillars…
- Sensor Fusion
- Object Detection
- Perception
AI for Autonomous Vehicles - CodeIntermediateNew
Multi-View Pose Estimation for a Sports-Analytics Startup
Use the publicly-released SoccerNet or a synthetic 4-view dataset (you can render with Unity or use a provided one). Implement a 2D pose estimator per view (HRNet or YOLOv8-pose…
- Pose Estimation
- Multi View Geometry
- 3d Reconstruction
Computer Vision - CodeIntermediateNew
Restore Smartphone Low-Light Photos for a Consumer AI App
You receive 200 paired low-light / well-lit phone photos plus 1,000 unpaired low-light photos. Build a pipeline that combines a learned denoiser (e.g. a small DnCNN-style model …
- Image Restoration
- Denoising
- Tone Mapping
Image Processing and Computational Imaging Build a verifiable portfolio.
Submissions become evidence. Reviewers with shipping experience score against a rubric; the result becomes a credential anyone can verify.
Why Ewance
- AnalysisIntermediateNew
Benchmark Visual SLAM Stacks for an Indoor Delivery Robot
You receive 8 indoor rosbag recordings (about 90 minutes total) captured by the robot's stereo camera + Inertial Measurement Unit (IMU) plus ground-truth trajectories from an ex…
- Visual Slam
- Sensor Fusion
- Trajectory Evaluation
Robot Perception and Autonomy - AnalysisIntermediateNew
Detect Defects on a Production Line for a Tier-1 Auto Supplier
You receive 12,000 labelled grayscale part images (8,000 good, 4,000 defective across 6 defect types) at 2048x2048. Build a pipeline that does classical preprocessing (illuminat…
- Defect Detection
- Cnn Classification
- Image Preprocessing
Image Processing and Computational Imaging - CodeSeniorNew
Train a GAN for Synthetic Defect Augmentation on a Factory Line
You receive a labeled defect dataset (12 defect types, ranging from 8 to 4,200 examples each), the production classifier, and a starter StyleGAN2-ADA codebase. Train a GAN per r…
- Gans
- Stylegan
- Data Augmentation
Generative AI - CodeBeginnerNew
Segment Solar Panels in Aerial Imagery for an Energy Audit Startup
You receive 600 labelled 1024x1024 orthophoto tiles (panel masks) and 1,000 unlabeled tiles. Train a segmentation model (U-Net or DeepLabV3+ baseline), validate at 0.85 IoU on a…
- Semantic Segmentation
- U Net
- Aerial Imagery
Image Processing and Computational Imaging - AnalysisSeniorNew
Brain-Tumor MRI Segmentation Bake-Off
You receive a curated public multi-modal MRI brain-tumor cohort (~600 patients, T1/T1c/T2/FLAIR with whole-tumor / tumor-core / enhancing-tumor masks). Train all three architect…
- Medical Imaging
- Segmentation
- Neural Networks
Machine Learning for Imaging and Medical Image Analysis - CodeBeginnerNew
Image Search for a DTC Furniture Retailer's App
Use a pretrained vision-embedding model (CLIP ViT-B/32 or DINOv2-small). Index a catalog of around 1,500 furniture images. Curate a small evaluation set of around 50 user-style …
- Image Embeddings
- Vision Transformers
- Image Search
Computer Vision (Undergraduate) - CodeSeniorNew
Train a 3D Object Detector for Highway Trucking
Use the nuScenes or Waymo Open Dataset (open access) as your training and evaluation source. Fine-tune a strong baseline (e.g., CenterPoint or BEVFusion) and define an evaluatio…
- Object Detection
- Perception
- Pytorch Or Tensorflow
AI for Autonomous Vehicles - CodeIntermediateNew
3D Reconstruction of Cultural Artifacts from Photo Sets
Use COLMAP (open-source SfM) + OpenMVS (open-source MVS) on a curated dataset of 5 small artifacts plus a calibration cube. Build a single Python CLI that ingests a folder of im…
- Structure From Motion
- Multi View Stereo
- 3d Reconstruction
Computer Vision - CodeIntermediateNew
Scene-Graph Generation for Retail Shelf Audits
You receive 1,500 labeled shelf photos (anonymized product crops, bounding boxes, and ~12 relation types). Build a pipeline that, for a new shelf photo, outputs (a) detected pro…
- Scene Graph Generation
- Object Detection
- Relation Prediction
Visual Intelligence and Visual Reasoning - CodeFoundationalNew
Edge Detection Pipeline for a Manufacturing QA Camera
Use a small provided dataset of around 200 part images under 3 lighting conditions. Build a classical pipeline using OpenCV: grayscale + adaptive thresholding + Canny edge detec…
- Image Processing
- Edge Detection
- Opencv
Computer Vision (Undergraduate) - CodeIntermediateNew
Reconstruct a Heritage Facade with Structure-from-Motion
You receive 250 phone photos of the facade plus 6 ground control points measured by a surveyor (used only for metric scaling and validation, not for reconstruction). Run SfM to …
- Structure From Motion
- Multi View Stereo
- 3d Reconstruction
3D Vision and Multi-View Geometry - CodeIntermediateNew
Prototype a Computer-Vision QA Tool for a Robotics Manufacturer
As a 4-person team, build: (1) a labeling pipeline on around 2,000 component images (Label Studio is fine); (2) a transfer-learned classifier or a small segmentation model that …
- Computer Vision
- Transfer Learning
- Model Deployment
AI Software Engineering Group Project - AnalysisIntermediateNew
Compare Stereo Depth Methods for a Drone Inspection Startup
You receive 500 calibrated stereo pairs from a turbine inspection plus sparse LiDAR ground truth on each pair. Implement (or wrap) three depth estimators: OpenCV Semi-Global Mat…
- Stereo Depth Estimation
- Multi View Geometry
- Model Evaluation
3D Vision and Multi-View Geometry
How it works
From brief to credential, in six steps.
Step 01
Browse challenges aligned to your studies.
Step 02
Accept the one that fits your goals.
Step 03
Work through it with AI Copilot guidance.
Step 04
Submit for structured evaluation.
Step 05
Earn a verified credential.
Step 06
Add it to LinkedIn with one click.
Related roles you may want to explore
Browse all roles →AI Engineering
AI Engineer
Between a promising research paper and a feature people actually use sits a long, unglamorous bridge — and AI engineers build it. The job is taking models that work in notebooks and shaping them into systems that hold up under real traffic, real costs, and real users with messy questions. Good work here looks like a retrieval pipeline that gets answers right ninety-something percent of the time, with evaluation harnesses catching regressions before they ship. Students grow into this role by treating Python and PyTorch as instruments rather than checkboxes, then learning how to reason about latency, evaluation, and cost together. If you enjoy stitching ideas into running software, this path will feel like home.
AI Engineering
Machine Learning Engineer
A model that works on a laptop and a model that works for millions of users are two very different artifacts, and machine learning engineers live in the gap between them. The role exists to take research-grade ML and turn it into reliable production systems, which means caring about latency, retraining pipelines, and what happens when the data distribution shifts at three in the morning. Students grow into this through hands-on work with PyTorch or TensorFlow plus enough software engineering discipline to run real CI/CD. Tools like AWS SageMaker become part of the workflow. Strong ML engineers can talk shop with data scientists on one side and platform engineers on the other, and that bilingual quality is often what gets them hired.
AI Engineering
MLOps Engineer
Models in production fail in stranger ways than models in notebooks ever could. The MLOps engineer is the person who anticipates those failures and builds the scaffolding that makes machine learning survive contact with real users. Think feature stores that stay consistent between training and serving, deployment pipelines through MLflow that make rollbacks boring, and observability that catches drift before stakeholders notice. The work sits at the intersection of platform engineering and data science, and rewards people who like building tools other engineers will rely on. A student grows into this role by getting comfortable with Kubernetes early and developing taste for what a healthy ML system actually looks like under load.
AI Engineering
NLP Engineer
Language is messy. People misspell, contradict themselves, ask the same thing five different ways, and expect a machine to understand. NLP engineers build the systems that try. The role spans classical text processing in spaCy, modern retrieval-augmented architectures stitched together with LangChain, and the constant judgment calls about when to fine-tune, when to prompt, and when to fall back to rules. It rewards people who love both linguistics and systems thinking. Students grow into it through small projects — a question-answering bot over their notes, a classifier for their inbox — that surface the real failure modes of language models. Good NLP engineers obsess over evaluation as much as architecture.
AI Engineering
Prompt Engineer
Writing instructions for a model is a strange new craft. The words you choose, their order, the examples you include — all shape what a multi-billion-parameter system actually does next. Prompt engineers treat this as a real engineering discipline: versioning prompts in tools like PromptLayer, running evaluations across thousands of test cases, optimizing for cost and latency in production, and collaborating with domain experts to encode their judgment in text. The role is new enough that students often help define it on the job. Growing into it means building intuition for how models fail, when to fine-tune instead, and how to write specs precise enough to ship. Good prompt engineers measure everything and trust vibes only as a starting point.
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