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.
- CodeIntermediateNew
Run a Monte Carlo Tree Search Strategy for a Robotics Pick-and-Place Task
You receive a simulator of the pick-and-place task: a bin with 10 randomly-placed parts, an action space of which part to pick next, and a reward = parts picked per minute with …
- Monte Carlo Tree Search
- Planning
- Simulation
Open coursework - CodeIntermediateNew
Build a Hybrid Recommender for a Niche Consumer-AI Music App
You receive listening events (around 240 million plays) plus a content embedding per track (audio + curator tags). Build a collaborative filtering model (ALS or implicit-feedbac…
- Recommender Systems
- Collaborative Filtering
- Content Based Filtering
Data Mining and Knowledge Discovery - CodeIntermediateNew
Build a Forgetting-Curve-Aware Spaced-Repetition Engine
You receive 6 months of practice logs from 8,000 learners (item, timestamp, response correctness, response latency). Fit a learner-personalized forgetting model (logistic per-it…
- Spaced Repetition
- Personalization
- Behavioral Data
Open coursework - CodeIntermediateNew
Build a GAN-Based Defect Generator for a Hardware Manufacturing Line
You receive around 60,000 good-unit images and around 380 defective-unit images across 4 defect classes. Train a class-conditional GAN (StyleGAN2-ADA or a smaller alternative fo…
- Gans
- Class Conditional Generation
- Data Augmentation
Deep Generative Models 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
- ResearchIntermediateNew
Planning Under Uncertainty for a Last-Mile Delivery Fleet
Build a simulator of the 50-block area with stochastic travel times conditioned on weather and time-of-day. Implement value iteration (for a small state space), MCTS (Monte Carl…
- Planning Under Uncertainty
- Markov Decision Processes
- Monte Carlo Tree Search
Open coursework - ResearchSeniorNew
Open-Vocabulary Segmentation Benchmark for a Robotics R&D Lab
Use a curated 200-image household scene set (publicly-available HM3D renderings or COCO + a handful of household prompts). Benchmark 3 open-vocabulary segmentation models: SAM +…
- Open Vocabulary Segmentation
- Vision Language Models
- Benchmarking
Computer Vision - CodeIntermediateNew
Predict Loan Default Risk for a Cross-Border Fintech
You receive 18 months of transactions (around 12M rows) and seller-firmographic data. Define a defensible proxy label for default (e.g., a 60-day chargeback-or-dispute spike com…
- Feature Engineering
- Model Selection
- Model Evaluation
Open coursework - ResearchSeniorNew
SAT-Based Planner for Smart-Grid Demand Response
Encode the dispatch problem (which customers to curtail by how much, respecting per-customer contractual caps and grid-cell totals) as a SAT or MaxSAT instance. Solve 50 histori…
- Sat Based Planning
- Constraint Encoding
- Benchmarking
Automated Planning - 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.
- ResearchIntermediateNew
Train a NeRF for Real-Estate Virtual Tours
You receive a curated dataset of 3 apartments, each with around 120 input images and known camera poses (already SfM-processed). Train a NeRF variant (Instant-NGP or Nerfacto re…
- Neural Scene Representation
- Nerf
- Pytorch
Open coursework - CodeIntermediateNew
Triage Medical-Imaging Annotations with a Small Vision Model
Train a binary normal/abnormal classifier on the public CheXpert or NIH ChestX-ray14 dataset. Use temperature scaling to calibrate the output, then define abstention thresholds …
- Image Classification
- Transfer Learning
- Calibration
Applied Machine Learning - DesignIntermediateNew
Train a Self-Play Agent for a Card-Game Edtech Demo
Implement a small two-player imperfect-information card game (Kuhn poker or a 3-card simplified Hold'em variant). Implement CFR or CFR+ for the game and run self-play for at lea…
- Counterfactual Regret Minimization
- Self Play
- Game Theory
Open coursework - ResearchSeniorNew
DPO Preference-Tune a Code Assistant for Style Compliance
You receive a 7B coding base model, a client's published code-style guide (Python, around 80 pages), and a generated preference dataset (4,000 pairs of code snippets where one m…
- Dpo
- Preference Optimization
- Fine Tuning
Fine-Tuning Large Language Models 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
- CodeIntermediateNew
Train a Multimodal Classifier for Medical Triage
Pick a fusion architecture (early fusion via cross-attention, late fusion via score combination, or a unified multimodal encoder like FLAVA/CoCa). Train on the 14,000 pairs with…
- Multimodal Fusion
- Cross Attention
- Pytorch
Open coursework - DesignSeniorNew
Design a Distributed Training Job for a 13B-Parameter Model
Decide whether to use Fully Sharded Data Parallel (FSDP), Tensor Parallelism, Pipeline Parallelism, or a hybrid; justify against the 13B-param + 32-H100 setup. Calculate memory …
- Distributed Training
- Fsdp
- Pytorch
Open coursework - ResearchIntermediateNew
Reproduce a Vision-Model Paper Under a Reproducibility Standard
Pick a vision-model paper from CVPR or NeurIPS 2024-2025 with publicly available code and a manageable compute footprint (single-GPU under 24 hours). Reproduce the headline metr…
- Reproducibility
- Experimental Design
- Model Evaluation
AI Measurement and Evaluation - CodeIntermediateNew
Build a Vision-Language Search for an E-commerce Catalog
Pick a vision-language encoder (OpenCLIP, SigLIP, or BLIP-2 image-text variant). Index all 600k product images into a vector database (Qdrant/FAISS). Build a query-time pipeline…
- Vision Language Models
- Clip
- Vector Search
Open coursework - ResearchSeniorNew
Curriculum RL for a Simulated Drone Inspection Task
You receive a PyBullet-based wind-turbine inspection simulator with parameterizable wind, blade orientation, and sensor noise. Design a 3-stage curriculum: (1) hover near a stat…
- Ppo
- Curriculum Learning
- Deep Rl
Open coursework - ResearchSeniorNew
Reproduce a Mechanistic Interpretability Result on a Small Transformer
Pick a published mechanistic-interpretability paper that operates on a small (under 1 billion parameter) open-source transformer (e.g., GPT-2 small, Pythia 70M). Set up the envi…
- Mechanistic Interpretability
- Transformer Internals
- Pytorch
AI Safety and Alignment - AnalysisBeginnerNew
Optimize Hyperparameters with Bayesian Optimization on a Tight Budget
You receive a B2B-SaaS churn dataset (about 12,000 customer-month rows, 38 features) and a fixed sweep budget of 40 trials per model family. Implement a Bayesian optimizer (Optu…
- Bayesian Optimization
- Hyperparameter Tuning
- Ensemble Methods
Advanced Machine Learning - AnalysisIntermediateNew
Imitation Learning from Human Demos for a Drone Inspection
You receive 6 hours of expert pilot demonstrations (state-action pairs at 20 Hz) recorded in an AirSim wind-farm environment with 3 turbine designs, plus a held-out 4th turbine …
- Imitation Learning
- Behavioral Cloning
- Dagger
Deep Reinforcement Learning - ResearchSeniorNew
Long-Context QA Evaluation Benchmark for Legal Memoranda
You receive 25 anonymized legal memoranda (50-90 pages each) and 100 QA pairs whose answers are deliberately spread across the documents (25 in pages 1-20, 25 in pages 20-40, 25…
- Long Context Qa
- Benchmark Design
- Model Evaluation
Open coursework - ResearchSeniorNew
Price American Options with a Deep Hedging Notebook
Simulate price paths for a single underlying (geometric Brownian motion is fine as a baseline; bonus for stochastic volatility). Implement Longstaff-Schwartz Monte Carlo as the …
- Deep Learning
- Stochastic Modeling
- Derivatives Pricing
AI and Quantitative Finance - ResearchIntermediateNew
Kernel Methods vs. Deep Learning on a Tiny-Data Drug-Discovery Task
You receive (or download) 3 public ADMET datasets from MoleculeNet (e.g., BBBP, Lipophilicity, FreeSolv). For each, train both: (a) a Gaussian process with a Tanimoto kernel ove…
- Kernel Methods
- Gaussian Processes
- Graph Neural Networks
Advanced Machine Learning - CodeBeginnerNew
Quantize a Vision Model for a Smart-Doorbell SoC
You receive a trained FP32 PyTorch person-detector (mAP 0.74 on a 5k validation set) plus a calibration dataset of 500 unlabeled doorbell frames. Convert to ONNX, then apply pos…
- Quantization
- Model Optimization
- Onnx
Open coursework - ResearchIntermediateNew
Lab Project: Compare Three Architectures on Your Own Mini-Benchmark
Scope the problem yourself (suggested examples: sentiment classification on a niche domain, tabular anomaly detection, time-series forecasting on a public dataset). Define the t…
- Experiment Design
- Statistical Testing
- Pytorch
Open coursework - ResearchIntermediateNew
Build a Generalization-Bound Tutorial for an MLE Onboarding Track
You will produce a Jupyter-notebook tutorial covering (1) sample-complexity intuition, (2) VC-dimension with worked examples for halfspaces and decision stumps, (3) Rademacher c…
- Statistical Learning Theory
- VC Dimension
- Rademacher Complexity
Statistical Machine Learning - CodeBeginnerNew
Build a Robust Image Classifier for a Climate-Tech Satellite Startup
You receive a labeled dataset of about 25,000 Sentinel-2 patches (positive = illegal construction visible, negative = not). The dataset is split by region AND by season so you c…
- Data Augmentation
- Deep Learning
- Pytorch
Advanced Deep Learning - CodeIntermediateNew
Teach a Warehouse Cobot from Operator Demonstrations
You receive a simulated UR5e cobot in PyBullet, plus 12 example demonstrations of two kitting sequences. Implement Dynamic Movement Primitives (DMPs — a classic LfD technique th…
- Learning From Demonstration
- Dynamic Movement Primitives
- Human Robot Interaction
Human-Robot Interaction - ResearchIntermediateNew
Train a Physics-Informed Neural Network for Heat Transfer in a Battery Pack
Solve the 2D unsteady heat-conduction equation on a square cell cross-section with a localized source and Dirichlet boundary conditions on the casing. Implement a baseline finit…
- Physics Informed Neural Networks
- Partial Differential Equations
- Pytorch
AI for Science and Engineering - AnalysisIntermediateNew
Compare ML Compiler Stacks on a Vision Backbone
Take a frozen ResNet-50 (or similar) in ONNX. Compile and benchmark it via TensorRT on Jetson + GPU, ONNX Runtime on all three, OpenVINO on x86 CPU, and IREE on ARM if time allo…
- Ml Compilers
- Tensorrt
- Onnx
Open coursework - ResearchIntermediateNew
Policy-Gradient Trading Agent on Historical Data
You receive 5 years of daily OHLCV (Open/High/Low/Close/Volume) data for 5 large-cap stocks. Build an episodic environment where each episode is one calendar year and the agent'…
- Policy Gradients
- Reinforce
- Rl Evaluation
Reinforcement Learning - ResearchIntermediateNew
Evaluate a Knowledge-Graph-Augmented Recommender
You receive permission to use the public MovieLens 1M dataset plus a derived item-KG (movie -> genre, director, decade) built from Wikidata. Train two recommenders: a matrix-fac…
- Knowledge Graph Embeddings
- Recommender Systems
- Benchmarking
Open coursework - CodeIntermediateNew
Prune and Distill a Speech Model for a Hearable
You receive a trained 280 KB CNN keyword spotter (10 keywords + silence + unknown) with 96.1% top-1 accuracy on the Google Speech Commands test set. Apply structured pruning (ch…
- Pruning
- Knowledge Distillation
- Model Compression
Edge ML and On-Device Machine Learning - CodeIntermediateNew
Train a Deep Q-Network for Warehouse Robot Routing
You receive a Gymnasium-compatible warehouse simulator (50x50 grid, 8 dynamic obstacle pedestrians, 20 randomized pick locations) and a baseline A* planner script. Train a DQN a…
- Deep Q Learning
- Reinforcement Learning
- Pytorch
Open coursework - ResearchSeniorNew
Diffusion-Policy Imitation for Bimanual Cooking Tasks
You receive 300 teleoperated demonstrations of a bimanual pour-and-stir task in a Robomimic-style simulator, deliberately including 2 valid solution modes per task (left-pour-ri…
- Diffusion Policies
- Imitation Learning
- Multimodal Action Distributions
Robot Learning - CodeIntermediateNew
Actor-Critic for Energy-Storage Dispatch
You receive 3 years of hourly day-ahead price data and a Python simulator that models state of charge, round-trip efficiency, and a 1-day price forecast with documented uncertai…
- Actor Critic
- A2c
- Deep Rl
Open coursework - CodeIntermediateNew
Fine-Tune a Diffusion Model for an E-commerce Product Studio
You receive 1,200 curated product + lifestyle images across 6 product categories, a brand-style guide, and the company's current studio cost per image (around EUR 18). Fine-tune…
- Diffusion Models
- Stable Diffusion
- Dreambooth
Open coursework - ResearchIntermediateNew
Prototype a Normalizing Flow for Anomaly Scoring in Climate Sensor Data
You receive 12 months of multivariate sensor traces (8 channels per sensor, hourly). Train a Normalizing Flow (Real NVP or a small Neural Spline Flow) on a clean training window…
- Normalizing Flows
- Density Estimation
- Anomaly Detection
Open coursework - ResearchIntermediateNew
Probe a Pretrained Encoder for Linguistic Knowledge
Take BERT-base (or DeBERTa-v3-base). Run layer-wise probes across at least 3 linguistic tasks: part-of-speech tagging, dependency arc classification, and semantic role labeling.…
- Interpretability
- Probing
- Transformers
Open coursework - AnalysisIntermediateNew
Compare Kernel SVMs and Gradient Boosting on Imbalanced Tabular Data
You receive a 220,000-row anonymized loan-default dataset with mixed numeric and categorical features and a ~6% positive class. Train and evaluate (1) an RBF-kernel SVM with pro…
- Kernel Methods
- Gradient Boosting
- Model Selection
Machine Learning - CodeBeginnerNew
Build an MLP Baseline for Credit-Default Risk at a Fintech
You receive 18 months of anonymized credit-decision data (around 600,000 applications, 80 features) with a 90-day default label. Train an MLP with regularization (dropout, weigh…
- Mlp
- Regularization
- Tabular Deep Learning
Deep Learning - CodeIntermediateNew
Build an Audio-Visual Speaker Diarization Pipeline
Build the pipeline: face detection + active-speaker detection on video, voice-activity detection + speaker embeddings on audio, then a fusion step that ties tracks to detected f…
- Audio Visual Fusion
- Speaker Diarization
- Active Speaker Detection
Multimodal Machine Learning - ResearchIntermediateNew
Hands-on Lab: Reproduce a Recent SOTA Vision Paper
Pick one of three pre-approved 2025 papers (offered by the supervisor) with a known reference codebase you may consult but not copy. Re-implement the model and training loop in …
- Pytorch
- Paper Reproduction
- Experiment Design
Open coursework - ResearchIntermediateNew
Compare Kernel Methods to Trees on a Genomics Classification Task
You receive a curated benchmark of about 12,000 labeled variants with ~120 numerical + ~40 string features. Fit kernel SVMs (RBF, polynomial, string), random forest, and XGBoost…
- Kernel Methods
- Svm
- Tree Ensembles
Open coursework - CodeBeginnerNew
Reduce Dimensionality on Sensor Streams for a Mid-Cap Robotics OEM
You receive 120 robot-hours of windowed sensor data (5s windows, 240 channels) with labels for normal vs. one of four fault classes. Implement (1) PCA, (2) kernel PCA with an RB…
- Dimensionality Reduction
- Kernel Methods
- Autoencoders
Machine Learning - ResearchSeniorNew
Solve a POMDP for a Healthtech Diagnostic Pathway
You receive a simplified pathway: 5 possible underlying conditions, 8 possible diagnostic tests each with documented sensitivity and specificity, and an outcome payoff matrix fr…
- Pomdp Modeling
- Belief States
- Approximate Solvers
Decision Making Under Uncertainty - CodeIntermediateNew
Defect Detection on PCBs for a Hardware-AI Manufacturer
Use the publicly-available PCB defect dataset (e.g., DeepPCB or HRIPCB). Fine-tune a small object detector (YOLOv8n or RT-DETR-small) on the 6 defect classes. Evaluate mean Aver…
- Object Detection
- Transfer Learning
- Model Evaluation
Computer Vision - ResearchSeniorNew
Structure Learning for a Causal Network in Fintech Risk
You receive the 60-signal dataset and a short interview summary of risk analysts' beliefs about which signals influence which. Use a hill-climbing structure-learning algorithm w…
- Structure Learning
- Bayesian Networks
- Causal Modeling
Open coursework - CodeIntermediateNew
Build a Federated Learning Prototype Across Two Hospitals
Simulate two sites with non-IID data splits (one site skews older, the other younger). Implement FedAvg using Flower (or PySyft). Run for at least 50 communication rounds; repor…
- Federated Learning
- Fedavg
- Secure Aggregation
Open coursework - ResearchSeniorNew
Train a Small Diffusion Model for Synthetic Defect Generation
You receive 2,000 labeled defect images and 18,000 clean weld images. Train a small class-conditional latent diffusion model on the defect images (Hugging Face diffusers is fine…
- Generative Perception
- Diffusion Models
- Data Augmentation
Open coursework - CodeIntermediateNew
LoRA Fine-Tune a 7B LLM for Legal-Clause Extraction
You receive a curated extraction dataset (2,000 train, 500 val, 500 test contracts with span-level labels across 12 clause types) and a fine-tunable 7B base model (e.g., Llama-3…
- Lora
- Fine Tuning
- Parameter Efficient Tuning
Open coursework - CodeBeginnerNew
Ship a Churn-Prediction Mini-Project End to End
You receive a 12-month anonymized dataset of subscriber events (logins, lesson completions, payment history, support tickets) for around 200,000 users. Define churn precisely (n…
- Feature Engineering
- Model Evaluation
- Gradient Boosting
Open coursework - CodeSeniorNew
Profile and Cut Inference Cost on a Recommender at Scale
You receive (1) a frozen ONNX export of the production model, (2) a sample request trace of 24 hours at 1% sampling, and (3) a single A100-class GPU sandbox. Profile with NVIDIA…
- Gpu Profiling
- Model Quantization
- Inference Optimization
Open coursework - ResearchSeniorNew
Inductive Logic Programming for a Fraud-Rule Discovery Pilot
You receive a labeled fraud dataset (around 25,000 transactions, around 4% positive class), a feature schema (28 features including device, geo, behavioral history), and a basel…
- Inductive Logic Programming
- Symbolic Ai
- Rule Learning
Open coursework - CodeBeginnerNew
Forecast Hourly Energy Demand for a Microgrid Operator
You receive 24 months of hourly demand, weather (temperature, irradiance), and calendar data for the community. Build a probabilistic forecaster (e.g., quantile regression with …
- Probabilistic Forecasting
- Quantile Regression
- Deep Forecasting
Open coursework - CodeIntermediateNew
Build a Sequence Model for Sign-Language Word Recognition
You receive about 12,000 short (1-3s) webcam clips covering a 50-word vocabulary, with body+hand pose features pre-extracted (e.g., MediaPipe Holistic landmarks per frame). Buil…
- Sequence Models
- Transformer
- Pose Estimation
Machine Perception - CodeBeginnerNew
Behavior Cloning for a Pick-and-Place Manipulator
You receive 200 human teleoperated demonstrations (state + action trajectories) of picking 8 small electronic components from a tray and placing them at marked locations in a ro…
- Behavior Cloning
- Imitation Learning
- Manipulation
Open coursework - CodeIntermediateNew
Train a Sequence Model for Wearable-Telemetry Sleep Staging at a Healthtech
You receive 220 nights of wearable telemetry from 60 subjects with PSG ground-truth labels. Train three sequence models: an LSTM baseline, a 1D-CNN+GRU hybrid, and a small trans…
- Sequence Models
- Lstm
- Transformers
Deep Learning - CodeIntermediateNew
Variational Autoencoder for Synthetic Tabular Banking Data
You receive a 500K-row anonymized transaction dataset with 25 columns (mixed numerical + categorical). Train a VAE (TabVAE or a small custom model) with appropriate likelihoods …
- Variational Inference
- Deep Generative Models
- Synthetic Data
Open coursework - AnalysisBeginnerNew
Approximate Inference for a Topic Model on Customer Tickets
You receive 180,000 tickets (subject + body) spanning the last 18 months. Preprocess into a bag-of-words representation with sensible stopwords and bigrams. Fit a 20-topic LDA v…
- Variational Inference
- Latent Dirichlet Allocation
- Approximate Inference
Open coursework
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Browse challenges aligned to your studies.
Step 02
Accept the one that fits your goals.
Step 03
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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
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.
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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Skills and disciplines shown on this page are derived from the Ewance challenge catalogue. When the median annual salary is available for this role via Adzuna, it will be shown above with the sample size and country.
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