Skills to Put on a Resume for a Research Scientist

The skills real Research Scientist postings screen for

Pulled from our Research Scientist 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:Benchmark Long-Context Architectures on a Legal-Doc Retrieval TaskValidate a Foundation Model for Protein-Ligand Docking Acceleration

• 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

• 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

• Hugging Face Transformers

  Tool

  Hugging Face Transformers 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 Hugging Face Transformers, not just the tool name on a line by itself.

  Practice it:Benchmark Long-Context Architectures on a Legal-Doc Retrieval TaskPlan a Parameter-Efficient Fine-Tuning Strategy for a Big-Tech AI Lab

• Systems-language proficiency (Go, Rust, C++)

  Separates “can script” from “can build production infrastructure” — memory management, concurrency, and performance tradeoffs live here.

  Evidence, not just a bullet: Link a repo with a project in Go, Rust, or C++ that does something non-trivial with concurrency or performance, and say what you optimized.

  Practice it:Implement and Tune HyperLogLog for Trace-Stream Cardinality in GoAdd Idempotency Keys to a Payment Webhook System

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.

• 

  ResearchSeniorNew

  Benchmark Long-Context Architectures on a Legal-Doc Retrieval Task

  You receive a public legal-QA dataset (e.g., LongBench's legal split or similar) filtered to documents over 50,000 tokens. Implement or wrap 3 architectures: a sliding-window Tr…

  • Long Context Architectures
  • State Space Models
  • Transformers

  Advanced Deep Learning
• 

  ResearchSeniorNew

  Validate a Foundation Model for Protein-Ligand Docking Acceleration

  Pick 20 publicly available protein-ligand complexes from the PDBbind dataset (or similar public source). Use a published open-source structural foundation model (e.g., a Boltz-s…

  • Foundation Model Evaluation
  • Structural Biology
  • Model Validation

  AI for Science and Engineering
• 

  CodeIntermediateNew

  Train a VAE for Synthetic Tabular Data at a Healthtech Startup

  You receive a synthetic-but-realistic clinical-trial table (around 50,000 patients, 35 columns, mixed continuous and categorical). Train a tabular VAE (or TVAE/CTGAN as alternat…

  • Vae
  • Tabular Generation
  • Synthetic Data

  Deep Generative Models
• 

  ResearchSeniorNew

  Implement an Autoregressive Model for Anonymized Voice-Synthesis at a Defense Vendor

  You receive a public-domain speech dataset (LibriTTS subset, around 50 speakers) and a fixed evaluation protocol (speaker-identifiability AUC, emotion-preservation MOS proxy, in…

  • Autoregressive Models
  • Voice Conversion
  • Speech Synthesis

  Deep Generative Models
• 

  CodeSeniorNew

  Run a Backpropagation Bug-Hunt on an Open-Source RL Implementation

  You receive the offending fork (around 4,000 lines of PyTorch) and three known-failure seeds. Reproduce the NaN failure deterministically, instrument the forward and backward pa…

  • Backpropagation
  • Pytorch
  • Debugging

  Deep Learning
• 

  ResearchSeniorNew

  Model-Based RL for a Robotic Arm Pick-Place Task

  You receive a PyBullet pick-and-place environment (Franka Panda arm, 12 object types, randomized starting poses) and a SAC baseline that hits 85% success after about 1.5 million…

  • Model Based Rl
  • World Models
  • Reinforcement Learning

  Deep Reinforcement Learning