Skills to Put on a Resume for a Machine Learning Engineer

The skills real Machine Learning Engineer postings screen for

Pulled from our Machine Learning Engineer role taxonomy — not a generic list. Each one names what a recruiter reads into it and, more usefully, how to actually back it up.

• Apache Spark

  Signals you can process data at a scale that doesn't fit in a single machine's memory — a different skill from pandas-scale analysis.

  Evidence, not just a bullet: Reference a Spark job you wrote and the dataset size or transformation that actually needed distributed processing.

  Practice it:Migrate a 200TB Data Lake from Parquet to IcebergHalve a Daily Spark Bill Without Breaking the SLA

• scikit-learn

  Technical

  scikit-learn is one of the specific, verifiable skills recruiters screen for in this role — naming it plainly is what gets it past both an ATS keyword match and a human skim.

  Evidence, not just a bullet: Don't just list "scikit-learn" — attach one concrete example of using it: a project, a number, or an artifact a recruiter could actually look at.

  Practice it:Structured Prediction for Insurance Claim Triage

• Python or JavaScript

  Listing a language only matters if you can point to something it built — recruiters skim past “Python” unless there's a repo or project attached.

  Evidence, not just a bullet: Link a GitHub repo with a script that solves a real problem — data cleaning, an API integration, a small app — with a README that explains what it does.

  Practice it:Optimize Hyperparameters with Bayesian Optimization on a Tight BudgetBuild a Forgetting-Curve-Aware Spaced-Repetition Engine

• AWS or Azure

  Cloud fluency recruiters filter on almost by keyword-match — but “used AWS” and “architected on AWS” read very differently.

  Evidence, not just a bullet: Name the specific services you’ve actually configured — e.g. set up an S3 lifecycle policy, wrote a Lambda, configured an IAM role — not just used passively.

  Practice it:Implement Authenticated Encryption for a Document ServiceContainerize a Legacy Java Monolith and Move It to AWS ECS

• PyTorch or TensorFlow

  Naming a specific deep-learning framework — not just “machine learning” — says you’ve actually built and trained a model, not just called an API.

  Evidence, not just a bullet: Link a repo with a model you trained, even on a small dataset, and mention the architecture and one hyperparameter choice you made.

  Practice it:Train a NeRF for Real-Estate Virtual ToursPretrain a Small Vision Transformer with Self-Supervised Learning

• Docker

  Tool

  Shows you can ship something that runs the same on your laptop as it does in production — a basic but non-negotiable expectation now.

  Evidence, not just a bullet: Link a repo with a Dockerfile you wrote and explain one non-obvious choice in it (a multi-stage build, a specific base image, a health check).

  Practice it:Containerize a Legacy Java Monolith and Move It to AWS ECSMigrating a 40-Person SaaS Scale-Up to Cloud-Native Architecture

• Feature engineering

  Technical

  Feature engineering is one of the specific, verifiable skills recruiters screen for in this role — naming it plainly is what gets it past both an ATS keyword match and a human skim.

  Evidence, not just a bullet: Don't just list "Feature engineering" — attach one concrete example of using it: a project, a number, or an artifact a recruiter could actually look at.

  Practice it:Structured Prediction for Insurance Claim TriagePredict Loan Default Risk for a Cross-Border Fintech

• Git

  Tool

  Not really a differentiator on its own anymore, but a messy Git history (force-pushes over main, no commit messages) is a real red flag recruiters notice.

  Evidence, not just a bullet: Point to a repo with a clean, readable commit history — that's the evidence, more than the word “Git” on a line by itself.

  Practice it:Branch Predictor Study for a Cryptocurrency Hashing Library

• CI/CD Pipelines

  Proof you think about how code gets to production safely and repeatedly, not just that it eventually works on your machine.

  Evidence, not just a bullet: Link a pipeline config (GitHub Actions, GitLab CI) you wrote and mention what it automated — tests, linting, a deploy gate.

Every challenge below is an AI-generated practice brief — not a real client engagement — that produces a submission you can point to as evidence for the skills above.

• 

  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

  3D Vision and Multi-View Geometry
• 

  ResearchSeniorNew

  Pretrain a Small Vision Transformer with Self-Supervised Learning

  You receive 80,000 unlabeled 224x224 histology tiles plus 4,000 labeled tiles split into train/val/test. Pretrain a ViT-Small using a self-supervised method of your choice (DINO…

  • Self Supervised Learning
  • Vision Transformers
  • Pytorch

  Advanced Deep 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
• 

  AnalysisIntermediateNew

  Structured Prediction for Insurance Claim Triage

  You receive 18,000 historical claims with text, attachments-count, claim amount, customer tenure, and the ground-truth final routing bucket. Train a structured classifier (e.g.,…

  • Structured Prediction
  • Multi Class Classification
  • Model Evaluation

  Advanced Machine Learning
• 

  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
• 

  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