Lakshya

Artificial Intelligence

Frontier track ≈10% of the market

4 · Research Engineer / Scientist / Member of Technical Staff

The role everyone pictures. A tenth of the postings, and the hardest to enter laterally.

What the job actually is

Making empirical progress on model capability under a compute budget. In practice that is far more engineering than the word "research" suggests — data pipelines, training runs that fail at hour nine, evaluation harnesses, and a great deal of careful measurement. Member of Technical Staff is worth understanding as a convention rather than a level: several labs use it deliberately flat, so the title carries no seniority signal and you must ask about scope directly.

Titles this hides behind

Member of Technical StaffResearch Engineer Research ScientistML Researcher, Foundational Models Staff Research Engineer — CodeAI Research Engineer

Who is hiring it

Anthropic, OpenAI, Cohere, Mistral, and — the notable Indian entry — Sarvam, which is posting genuine foundation-model roles: ML Researcher for foundational models, ML Engineer for training infrastructure, and ML Engineer for data. That is new. Until recently, frontier research roles were essentially not available in India.

How to recognise it in a JD

Look for publications, NeurIPS, ICML, pre-training, RLHF, ablation, scaling laws. Only 6% of the corpus mentions publications at all, so when it appears it is a real filter rather than boilerplate.

The loop

Recruiter screen
Research coding screen Frequently a training loop or a data transform written from scratch, not a puzzle.
Deep dive on your own work — the decisive round Ninety minutes on one project you did. Expect three levels of "why did you choose that" on every decision, including ones you made casually.
Pair programming on a research task Open-ended, with the interviewer watching how you form and discard hypotheses.
Values / alignment discussion At safety-focused labs this is substantive and can be disqualifying, not a formality.

Prepare like this

  • Reproduce one paper end to end and be able to defend every hyperparameter. A single deeply-owned reproduction beats five shallow ones.
  • Interrogate your own past work to three levels before the deep dive. Why that architecture, why that baseline, why you believed the result.
  • Write a training loop from scratch in PyTorch or JAX, no trainer abstraction, until it is muscle memory.
  • Be honest about negative results. "We tried it, it did not work, here is what we learned" is a strong answer here and a weak one almost nowhere else.

Be ready for

  • "You have 100 GPU-hours and a hypothesis. Design the experiment." Testing whether you can scope research to a budget — the actual daily constraint.
  • "Your eval improved by 2 points. Convince me that is real." Variance, seeds, contamination, and whether the eval measures what you claim.
  • "What is the strongest argument against your own last result?"

Read this before you commit a month to it

This is the highest-effort, lowest-yield archetype for a lateral candidate. It is roughly a tenth of the market, screens partly on credentials you either have or do not, and competes against people with full-time research track records. If you have publications or a research-adjacent history, pursue it. If you do not, the same month invested in archetypes 1, 2 or 6 has a materially better expected outcome — and those roles pay comparably.

Compensation

United States — base$240k – $400k+ The top of the published range, and equity dominates total compensation at frontier labs.
India — total₹40L – ₹90L Estimated, thin sample. Sarvam is close to the only domestic data point.

What this role tests

Themes, and where to learn them

These chapters are shared across every role that tests them, so preparation here compounds rather than being spent once.

The controlOne habit separates strong candidates from plausible ones more reliably than any technical depth.READ THE CHAPTER →Measuring what resists measurementNamed in 59% of AI postings and 42% of platform postings. Almost nobody studies it deliberately.READ THE CHAPTER →Design under constraintThe decisive round for SRE, AI Platform, Infrastructure — and the reason strong engineers fail it.READ THE CHAPTER →

Practice questions across all themes →  ·  Back to Artificial Intelligence →