Information Technology Challenges
Explore information technology challenges on Ewance to develop skills companies are actively hiring for. Work on briefs covering cloud, infrastructure, security, and platform engineering.
Most Popular
- 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
Probabilistic Machine Learning - CodeIntermediateNew
Prompt-Injection Hardening for a Customer-Support Agent
You receive the current agent prompt, the pen-tester's 60-attack injection test set (direct prompt injection, indirect via doc content, refusal-bypass, and exfiltration), and a …
- Prompt Injection Defense
- System Prompt Design
- Red Team Operations
Prompt Engineering - CodeIntermediateNew
ReAct Agent for Legal-Research Tool-Use
You receive 30 research questions with paralegal-written gold answers and citation lists, plus stubbed implementations of the 4 tools (you do not need to build retrieval — just …
- React Prompting
- Tool Use
- Agent Design
Prompt Engineering - CodeIntermediateNew
Multi-Turn Dialogue Manager for a Banking Assistant
You receive a transcript dataset of 200 conversations (human-tagged with intent, slot values, and required outcome), a list of 8 supported intents, and tool stubs for 3 backend …
- Dialogue Management
- Intent Classification
- Slot Filling
Question Answering and Conversational Systems 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
- CodeIntermediateNew
Extractive QA on Clinical Trial Protocols
You receive 500 anonymized protocol PDFs (already OCR-ed to text) and 1,200 labeled question-answer pairs where each answer is an exact text span. Build an extractive QA system:…
- Extractive Qa
- Reading Comprehension
- Model Finetuning
Question Answering and Conversational Systems - 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 - 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
Reinforcement Learning - AnalysisIntermediateNew
Exploration Strategies for a Recommendation Bandit
You receive 60 days of anonymized impression/click logs covering around 200 content items and user features (cohort, listening history bucket). Build a contextual-bandit simulat…
- Contextual Bandits
- Thompson Sampling
- Ucb
Reinforcement Learning - 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.
- CodeIntermediateNew
RAG Faithfulness Evaluation for a Medical-Education Assistant
You receive 200 student-style questions, two RAG configurations (config A: vector-only + GPT-class generator; config B: hybrid + rerank + GPT-class generator), and the medical-t…
- RAG Evaluation
- Faithfulness
- LLM As Judge
Retrieval-Augmented Generation - CodeIntermediateNew
Agentic RAG with Context-Window Budgeting
You receive a synthetic dataset of 60 founder-style queries paired with 'workspaces' (each up to 500 documents across 3 source types), plus gold-standard answers and citation li…
- Agentic RAG
- Context Window Management
- Iterative Retrieval
Retrieval-Augmented Generation - CodeIntermediateNew
Train a Reward Model on Customer-Support Preferences
You receive 8,000 labeled preference pairs from real support conversations (each pair is two model responses with a human-chosen winner). Fine-tune a small open-weights base mod…
- Reward Modeling
- Preference Learning
- Bradley Terry Loss
Machine Learning from Human Preferences (RLHF and Alignment) - CodeIntermediateNew
DPO Fine-Tune for a Domain-Specific Writing Assistant
You receive a base instruction-tuned model checkpoint plus 2,500 preference pairs from editorial reviews (each pair: two grant-application paragraphs, the editor-preferred winne…
- Dpo
- Preference Learning
- Model Finetuning
Machine Learning from Human Preferences (RLHF and Alignment) 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
- ResearchIntermediateNew
Red-Team Evaluation of a Refusal Policy
You receive the lab's written refusal policy (version 2.3) and a starter set of 60 red-team prompts (10 per category). Extend the set to 240 prompts (40 per category) using docu…
- Red Team Operations
- Refusal Policy
- Alignment Evaluation
Machine Learning from Human Preferences (RLHF and Alignment) - CodeIntermediateNew
Constitutional AI Critique Loop for Hallucination Reduction
You receive the meal-planning prompts (60 test cases with dietary constraints), an unrevised baseline (single-pass instruction-tuned model), and an empty nutrition-constraint co…
- Constitutional Ai
- Self Critique
- Alignment Prompting
Machine Learning from Human Preferences (RLHF and Alignment) - ResearchIntermediateNew
Reward Shaping for a Quadruped Locomotion Policy
You receive a quadruped locomotion environment (Isaac Lab or pybullet-quadruped), the previous reward function (5 terms), and a budget of 6 training runs. Design 4 reward varian…
- Reward Shaping
- Ppo
- Locomotion
Robot Learning - 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 - 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 - CodeIntermediateNew
Design a Force-Controlled Polishing Skill for a Watchmaker
You receive simulated polishing trajectories from the manufacturer's robot, force-sensor logs from 20 master-craftsman demonstrations, and a quality-rubric (mirror finish 1-5) f…
- Impedance Control
- Force Control
- Manipulation
Robotics - ResearchIntermediateNew
Train a Reinforcement-Learning Locomotion Policy for a Quadruped
You receive a configured Isaac Lab environment for the quadruped, a baseline PPO trainer, and a set of 8 trip-hazard / slip stress scenarios. Train the policy for a budget of ab…
- Reinforcement Learning
- Locomotion
- Domain Randomization
Robotics - CodeIntermediateNew
Scale Feature Pipelines for a Hyperscaler Search-Ranking Team
You receive a synthetic-but-realistic 80 GB sample of the ranking events plus the existing Spark pipeline (PySpark) and a Spark UI snapshot from a recent production run. Profile…
- Apache Spark
- Distributed Systems Design
- Performance Profiling
Machine Learning at Scale - DesignIntermediateNew
Build a Feature Store for a Fintech Fraud Team
You will design a feature-store layer covering 12 representative fraud features (account-level, merchant-level, transaction-level), with both batch (Spark) and online (low-laten…
- Feature Stores
- Data Pipelines
- Apache Spark
Machine Learning at Scale - ResearchIntermediateNew
Benchmark Graph-Embedding Methods on a Climate-Network Dataset
You receive a 200M-edge sample of the knowledge graph and a labeled entity-similarity test set (5,000 pairs with relevance labels). Benchmark three methods: a shallow embedding …
- Graph Embeddings
- Neural Networks
- Scalable Ml
Machine Learning at Scale - ResearchIntermediateNew
Detect Coordinated Inauthentic Behavior on a News-Sharing Network
You receive a 60-day sample of about 6 million posts mentioning a recent election, with account metadata (creation date, posting times, follower graph). Design and prototype a C…
- Network Analysis
- Anomaly Detection
- Near Duplicate Detection
Social Network Analysis and Web Science - ResearchIntermediateNew
Audit Recommender Filter Bubbles for a Civic Forum
You receive 90 days of impression logs (about 30 million recommendation events) tagged with content viewpoint labels (left-leaning, center, right-leaning, non-political) from an…
- Recommender Evaluation
- Diversity Metrics
- Audit Methodology
Social Network Analysis and Web Science
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.
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