Principal Research Scientist, Synthetic Data Generation

What matters most

• Set direction for scalable synthetic-data pipelines

  The role sets technical direction across NVIDIA’s frontier-model efforts and builds LLM-based pipelines with automated quality evaluation. The datasets support pre- and post-training across reasoning, coding, structured output and multimodal understanding; throughput, distributed inference and cluster-scale cost are explicit concerns.

• Develop data for agentic and RL training

  The posting calls for synthetic trajectories, multi-turn interactions, function-calling data and executable environments, including reward modeling and verifiable-reward data.

• Advance multimodal generation with model teams

  Work with NVIDIA’s model teams on synthetic image, document, video and audio data, alongside text, code and structured data for LLM training.

• Make sensitive-data synthesis safer

  Advance differential privacy, anonymization and de-identification methods intended to enable training on sensitive data in regulated domains.

• Build durable open-source tools and lead research

  Define and maintain NeMo libraries and SDKs with clean APIs, documentation, configuration-based architecture and Git/CI-CD practices. The role also calls for original conference research, cross-team collaboration and mentoring scientists and engineers.

Likely challenges to think through

• Balancing data quality against generation cost

  Preparation hypothesis: stronger filtering and evaluation may improve training data but reduce throughput or increase cluster-scale cost. Prepare to explain how you would measure downstream model gains alongside evaluation reliability, distributed-inference throughput and cost.

• Making agentic data both varied and verifiable

  Preparation hypothesis: diverse tool-use trajectories can be valuable for training yet difficult to score consistently, while tightly constrained environments may limit coverage. Prepare to discuss executable checks, reward design and failure analysis for multi-turn interactions.

• Preserving utility without exposing sensitive information

  Preparation hypothesis: privacy protections can reduce the fidelity of synthetic data. Prepare to explain how you would evaluate both privacy risk and training utility, distinguishing guarantees such as differential privacy from anonymization or de-identification.

• Turning research advances into maintainable libraries

  Preparation hypothesis: fast-changing generation methods may conflict with stable APIs and reproducible releases for a broad developer community. Prepare examples of how you would make architectural trade-offs, document behavior and coordinate research and engineering work.

Topics to prepare

• Data across LLM training stages

  The posting requires understanding how synthetic data shapes pre-training, post-training and reinforcement learning. Exercise: outline distinct data objectives and failure modes for each stage, using reasoning, coding and structured-output examples.

• Synthetic-data quality evaluation

  The role pairs LLM-based generation with automated quality evaluation. Exercise: design a review plan for a generated coding dataset that separates validity, diversity, duplication and usefulness for training; explain what each check cannot establish.

• Agentic and reinforcement-learning data

  The posting names synthetic trajectories, multi-turn interactions, function calling, executable environments, reward modeling and verifiable-reward data. Exercise: trace one tool-use task from generated interaction through environment feedback to a verifiable reward, noting possible shortcuts.

• Multimodal synthesis

  The role covers image, document, video and audio data in partnership with model teams. Exercise: compare two of these modalities, identifying what must be generated, how labels or paired content could be checked, and where quality assessment differs.

• Privacy-preserving synthesis

  The posting explicitly calls for differential privacy, anonymization and de-identification for sensitive data. Exercise: contrast their protections and limitations in a hypothetical regulated-data dataset, including how you would test for residual disclosure risk.

• Research direction and evidence

  The principal role sets technical direction, publishes original research and works across research and model teams. Exercise: formulate a synthetic-data research question, specify a baseline, ablations and downstream training outcomes, then explain what evidence would justify changing direction.

Skills to revise

• Build a generation pipeline

  The posting calls for scalable LLM-based pipelines producing training datasets. Practice: implement a small configuration-driven flow that generates text or structured examples, runs automatic checks, records rejection reasons and exports accepted records.

• Profile distributed inference trade-offs

  Throughput, distributed inference and cluster-scale cost are explicit requirements; vLLM and TGI are named examples of inference frameworks. Practice: benchmark a small generation workload with one named framework, then report throughput, latency and estimated compute cost under different batching choices.

• Prototype tool-use training data

  The role includes function-calling data and executable environments. Practice: create a small deterministic environment and generate multi-turn tool-call trajectories; execute calls and verify that recorded outcomes and rewards match the environment.

• Design a maintainable NeMo-ecosystem library interface

  The posting calls for open-source libraries and SDKs within the NVIDIA NeMo ecosystem, with clean APIs and documentation. Practice: draft and implement a minimal configurable generation-and-evaluation interface with examples and tests, without assuming an unstated NeMo API.

• Exercise Git and CI/CD discipline

  Professional Git/CI-CD practices are stated responsibilities. Practice: prepare a focused library change with tests, documentation and a CI check, then write a short review note explaining compatibility and failure handling.

• Draft a technical-direction brief

  The role sets direction for synthetic data generation in the NeMo ecosystem. Prepare a one-page proposal connecting text, code, structured, and multimodal data to LLM pre-training, post-training, and RL; label it as your proposed approach, not NVIDIA’s plan.

• Build a project walkthrough

  Select a real synthetic-data or generative-modeling project, if available. Document your research contribution, engineering decisions, dataset quality evaluation, training impact, and what the evidence does and does not show.

• Practice a pipeline design exercise

  Sketch an LLM-based generation pipeline with automated quality checks. Be ready to explain distributed inference using a framework such as vLLM or TGI, plus throughput and cluster-scale cost trade-offs.

• Prepare an agentic-data example

  Work through a proposed multi-turn, function-calling trajectory and an executable RL environment. Specify how you would check outcomes and construct reward-modeling or verifiable-reward data.

• Map multimodal and privacy trade-offs

  Prepare a short comparison of image, document, video, and audio synthesis challenges. Include when differential privacy, anonymization, or de-identification might be appropriate for sensitive data, without claiming those methods eliminate risk.

• Assemble open-source evidence

  Bring examples, if available, of libraries or SDKs you developed or maintained. Identify API and documentation choices, configuration, Git/CI-CD practices, and verifiable signs of developer adoption.

• Summarize research and leadership evidence

  Prepare a publication list with each contribution stated precisely, plus examples of mentoring or cross-team decisions you influenced. The posting specifically calls for a strong research record and mentoring scientists and engineers.

• Write questions for the employer

  Ask how the team prioritizes data modalities, measures downstream training gains, divides work between NeMo libraries and model teams, and evaluates privacy and quality trade-offs.