Skills to Put on a Resume for an ML Researcher

The skills real ML Researcher postings screen for

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

• 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 ToursCompare Stereo Depth Methods for a Drone Inspection Startup

• A/B Testing

  Methodology

  A/B testing is proof you think in experiments, not opinions — you can design a test, pick a metric, and read a result without over-claiming significance.

  Evidence, not just a bullet: Describe one test end to end: the hypothesis, the metric that moved (or didn't), and what you'd do next.

  Practice it:Measure HTTP/3 vs HTTP/2 Video Delivery Over CellularMine Basket History to Justify Grocery Shelf-Adjacency Changes

• Ablation study design

  Methodology

  Ablation study design is a named method, not a vague competency — claiming it says you can apply a specific, repeatable approach, not just "think analytically."

  Evidence, not just a bullet: Walk through one real case where you applied Ablation study design step by step, including what the output was.

  Practice it:End-to-End Lane Following on a Donkeycar PlatformPre-Register and Run a Small Neural-Network Ablation Study

• Attention mechanisms

  Methodology

  Attention mechanisms is a named method, not a vague competency — claiming it says you can apply a specific, repeatable approach, not just "think analytically."

  Evidence, not just a bullet: Walk through one real case where you applied Attention mechanisms step by step, including what the output was.

  Practice it:Build an Attention-Visualization Tool for Translation Quality Audit

• Distributed training

  Technical

  Says you can train models across multiple GPUs or machines, not just on a single laptop — a scale problem distinct from the modeling itself.

  Evidence, not just a bullet: Reference a training run you scaled across devices and one bottleneck you had to work around (data loading, gradient sync).

  Practice it:Design a Distributed Training Job for a 13B-Parameter ModelAuto-Tune a Distributed Training Cluster's Throughput

• MLflow

  Tool

  MLflow is a specific tool real postings for this role name by name — listing it precisely (not folded into "familiar with standard tools") is what gets it past an ATS keyword match.

  Evidence, not just a bullet: Point to one real thing you built or produced with MLflow, not just the tool name on a line by itself.

  Practice it:Automate Retraining with a Drift-Triggered MLflow Pipeline

• Fine-tuning

  Technical

  Shows you can adapt an existing pretrained model to a specific task instead of training from scratch — the practical, resource-efficient skill most real ML work actually uses.

  Evidence, not just a bullet: Reference a model you fine-tuned, the base model you started from, and what changed in performance.

  Practice it:Natural Language Inference for an HR-AI Compliance ToolFine-Tune a Diffusion Model for a Sustainable-Fashion Mood-Board Tool