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Research Scientist

This is not a traditional model-tuning or applied-ML role. We need someone who understands frontier capability iteration — post-training, eval, agent training, and data strategy — and can also analyze how models fail in real business scenarios. Your core output is research judgment, evaluation methods, data strategy, and experimental conclusions. You’ll independently own a research or data direction, turning fuzzy problems into clear, deliverable benchmarks, data products, or training-feedback plans.

What you’ll do

  1. 01Track leading model teams’ needs and movements across post-training, agent training, eval, RL environments, and synthetic data.
  2. 02Build an iteration loop from training feedback to data strategy, grounded in model evals, customer feedback, and error analysis.
  3. 03Independently own a research or data direction — problem definition, literature review, experiment design, data construction, evaluation methods, and retrospectives.
  4. 04Systematically analyze model failure modes in real tasks: understanding, reasoning, tool use, multi-turn interaction, visual recognition, professional judgment, task planning, and result reliability.
  5. 05Design benchmarks, evals, rubrics, quality standards, and training-data plans that genuinely serve capability gains.
  6. 06Work with domain experts, algorithm, data-engineering, and delivery teams to decompose expert judgment and real workflows into learnable, evaluable task units.

What we look for

  1. 01Master’s or above in AI, CS, statistics, math, or a related field.
  2. 02Experience with LLMs, NLP, multimodal, agents, model eval, post-training, data construction, or training-data analysis.
  3. 03Understanding of SFT, RLHF / RLAIF, preference data, reward models, eval, agent training, and synthetic data.
  4. 04Solid research training — paper reading, experiment design, data analysis, error attribution, and technical writing.
  5. 05Strong coding ability — Python and common ML / data tools; able to build model calls, data processing, eval scripts, and experiment analysis.
  6. 06Strong ownership — able to break fuzzy problems into clear goals, experimental paths, and deliverables.

Nice to have

  • Conference papers, high-quality arXiv / workshop work, open-source projects, or research internships.
  • Experience at a top AI lab, big-tech model team, research institution, or strong startup.
  • Experience with post-training, benchmarks, eval, agent / multimodal / code evaluation, or high-quality dataset construction.
  • Your own views on capability boundaries, synthetic data, agent training, RL environments, or expert data.
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