New opportunity

Member of Technical Staff, Pretraining

LambdaQ Labs · Pune, India

About this role

LambdaQ Labs is hiring a Member of Technical Staff, Pretraining in Pune, India, to train the foundation models behind its Vikasit products. The role spans data curation and tokenization, large-scale distributed training and post-training, with a focus on improving model quality while keeping inference costs manageable.

Responsibilities

Own parts of the pretraining pipeline, including data, model architecture or training infrastructure.
Run and analyze large-scale training on multi-node GPU clusters.
Improve data quality, mixtures and curricula for Indian and global use.
Design experiments that measurably improve benchmarks and downstream quality.

Requirements

Strong PyTorch and distributed-training experience with FSDP, DeepSpeed or Megatron.
Solid understanding of transformer internals, optimization and scaling laws.
Experience training models at 1B+ scale, or equivalent research depth.

Preferred

MoE training experience, tokenizer or data-pipeline work, and publications at top machine-learning venues. This is a full-time research position. The company states that it hires for skill over credentials and welcomes links and projects from applicants.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

PyTorchDistributed trainingFSDPDeepSpeedMegatronTransformersModel optimizationScaling lawsGPU clustersData curationTokenizationMixture of ExpertsData pipelinesExperiment design

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Own a pretraining component
The role calls for ownership of data, model architecture, or training infrastructure within the pretraining pipeline, rather than work on training runs alone.
Run and analyze distributed training
Multi-node GPU training is a stated responsibility. The posting asks for strong PyTorch and distributed-training experience with FSDP, DeepSpeed, or Megatron, plus 1B+ model-training experience or equivalent research depth.
Improve data for Indian and global use
Data quality, mixtures, and curricula are explicit priorities; the role also spans data curation and tokenization.
Prove model-quality gains experimentally
Experiments should produce measurable improvements in benchmarks and downstream quality. Transformer internals, optimization, and scaling laws underpin this work.
Balance quality against inference cost
The role aims to advance the Vikasit models while keeping inference economics manageable. MoE training is listed as a nice-to-have, not a requirement.

Likely challenges to think through

Quality gains versus serving cost
Preparation hypothesis: a training change that improves benchmarks may also raise inference cost. Prepare to explain how you would compare quality gains with the resulting serving-cost trade-off; the posting does not specify a target metric.
Regional coverage versus data-mixture quality
Preparation hypothesis: improving performance for Indian use while retaining global quality may require difficult mixture and curriculum choices. Prepare a way to test those choices against both benchmark and downstream outcomes.
Reliable runs versus experiment speed
Preparation hypothesis: multi-node runs can make failures and ambiguous results costly. Prepare to discuss how you would diagnose training behavior and isolate the effect of a change before committing more compute.

Practice your answers

Technical practice

How would you assess and improve the quality of a pretraining corpus?

What to coverThe role includes data curation for Indian and global use. Suggested preparation: discuss quality signals, filtering trade-offs, and how you would test whether changes improve downstream quality rather than just corpus statistics.

How would you evaluate a tokenizer for Indian and global language coverage?

What to coverTokenizer work is listed as a plus, and the role spans Indian and global data. Suggested preparation: explain how token efficiency, language coverage, and compatibility with an existing model would affect the choice.

How would you choose a data mixture and curriculum for pretraining?

What to coverImproving mixtures and curricula is an explicit responsibility. Suggested preparation: describe controlled comparisons across data sources or training stages, including how you would detect gains in one use case that mask losses in another.

Which transformer architecture choices most affect training quality and inference cost?

What to coverThe posting asks for transformer internals and ownership of architecture work, with attention to inference economics. Suggested preparation: connect architectural trade-offs to measurable model quality, training resources, and serving cost.

How would optimization and scaling laws guide a large training run?

What to coverOptimization, scaling laws, and experience at 1B+ scale or equivalent research depth are stated requirements. Suggested preparation: explain how you would use smaller runs to inform a larger run while recognizing where extrapolation may fail.

How would you configure and validate distributed training in PyTorch?

What to coverThe posting names PyTorch and FSDP, DeepSpeed, or Megatron. Suggested preparation: compare relevant sharding and parallelism trade-offs, then explain how you would check that distributed training is both correct and efficient.

How would you diagnose a multi-node GPU training run that slows down or becomes unstable?

What to coverRunning and analyzing large-scale training on multi-node GPU clusters is a stated duty. Suggested preparation: describe a systematic way to separate data-loading, communication, memory, and optimization issues using run measurements.

What would you examine before using a mixture-of-experts model?

What to coverThe role headline mentions a frontier MoE, and MoE training experience is listed as a plus. Suggested preparation: discuss routing and load-balancing considerations alongside quality, training complexity, and inference cost.

How would you establish that a pretraining experiment improved the model?

What to coverThe role calls for experiments that measurably improve benchmarks and downstream quality. Suggested preparation: outline a comparison with clear baselines and controls, and explain how you would investigate conflicting evaluation results.

How would you assess a post-training change against pretraining quality and serving cost?

What to coverThe role spans pretraining and post-training while emphasizing manageable inference economics. Suggested preparation: explain which quality and cost measures you would compare, without assuming a particular post-training method.

Behavioural practice

Tell me about a time you took ownership of a model-training pipeline component.

What to coverThe role involves owning parts of the data, architecture, or training infrastructure pipeline. Suggested preparation: use a real situation, your specific actions and decisions, and the resulting evidence of impact.

Describe a difficult training-run problem you investigated.

What to coverAnalyzing large-scale, multi-node training runs is part of the advertised work. Suggested preparation: give the situation, how you narrowed down the cause, what you did, and the observed result.

Tell me about an experiment that did not produce the improvement you expected.

What to coverThe posting emphasizes measurable benchmark and downstream improvements. Suggested preparation: explain the hypothesis, your analysis of the outcome, what you changed or ruled out, and the result.

Describe a time you had to balance competing model-quality goals.

What to coverThe role targets Indian and global use while asking for gains in benchmarks and downstream quality. Suggested preparation: provide a real situation, the evidence and trade-offs behind your action, and what the outcome showed.

Sharpen your knowledge

Topics to prepare

Transformer internals
Required: review attention, normalization, residual connections and positional information. Exercise: draw a transformer block and explain how each component affects training behavior; the posting calls for a solid grasp of transformer internals.
Optimization and scaling laws
Required: revise optimizer behavior and the tradeoffs among model size, data and training compute. Exercise: compare two hypothetical training budgets and justify which experiment would better test quality per unit of cost; the role targets model quality while keeping inference economics manageable.
Distributed-training strategies
Required: compare how FSDP, DeepSpeed or Megatron approaches distribute model state and training work. Exercise: explain likely memory and communication bottlenecks in a multi-node run; the role runs and analyzes training on multi-node GPU clusters.
Data mixtures and curricula
Responsibility: improve data quality, mixtures and curricula for Indian and global use. Exercise: propose a small mixture change, state the quality hypothesis and identify which downstream results would test it.
Mixture-of-experts tradeoffs
Preferred, not required: MoE training experience. Exercise: explain how expert routing could affect training stability, capacity and inference cost; the page describes a frontier MoE and emphasizes cost per unit of quality.

Skills to revise

Implement a distributed PyTorch training step
Exercise: build or revisit a small transformer training run in PyTorch using one posting-named approach—FSDP, DeepSpeed or Megatron—and check that loss and saved state behave as expected. Strong PyTorch and distributed-training experience is required.
Diagnose a multi-node run
Exercise: inspect metrics or logs from an available distributed run and summarize throughput, memory pressure, instability and the next test you would run. This practices the advertised responsibility to run and analyze large-scale training.
Test a data-pipeline change
Exercise: take a sample corpus, define quality filters and mixture proportions, then compare token counts and sample outputs before and after the change. Data quality is a responsibility; tokenizer and data-pipeline work are listed as nice to have.
Design a measurable training experiment
Exercise: write a one-page experiment plan with a baseline, one controlled change, benchmark and downstream measures, and a decision rule. The posting explicitly asks for experiments that measurably improve quality.
Explain a model-scale training decision
Exercise: prepare a technical account of an actual 1B+ training run or equivalent research project, if you have one; otherwise, practice outlining a proposed run without presenting it as completed work. The requirement is 1B+ training experience or equivalent research depth.

Your preparation checklist

Prepare a pretraining project walkthrough
Select a project or research example, if available, and prepare a one-page account of your contribution, model scale, decisions, results and limitations. The role asks for 1B+ model-training experience or equivalent research depth.
Explain a distributed training run
Prepare a diagram and brief account of a PyTorch run using FSDP, DeepSpeed or Megatron, if you have one. Cover multi-node setup, performance or stability issues, and how you analyzed the run; these are stated requirements and responsibilities.
Show a data-quality decision
Prepare an example, or a clearly labeled hypothetical, showing how you would assess data quality, tokenization, mixtures or curricula for Indian and global use. State the checks and trade-offs you would examine.
Draft a measurable experiment
Outline one proposed pretraining experiment with a hypothesis, baseline, benchmark and downstream evaluations, and a decision rule. Include how you would weigh quality gains against training resources and inference cost.
Rehearse architecture and scaling trade-offs
Prepare a short explanation of transformer internals, optimization and scaling laws as they affect a training decision. If relevant to your experience, add MoE trade-offs; MoE experience is listed as a nice-to-have.
Write focused questions for the team
Bring questions about ownership across data, architecture and infrastructure; how training runs are evaluated; and how the team balances model quality with inference economics. These are preparation questions, not stated company processes.

Availability last checked: 19 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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