Datology AI

Datology AI

Research Scientist

Redwood City · Mid

Sponsorship not specified$180k-$300kDetected 179 days ago
SnowflakeMachine LearningDeep LearningPyTorchSparkData EngineeringResearchCommunicationCollaborationAdaptability

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odds of building a lasting career here

55Sponsors, lottery-bound
Cap-exempt (no lottery)0
Sponsors this role68
Entry-level history0
PERM / green-card track0
Lottery odds (Level IV)94
Fits your clock70

Sponsors, but it's cap-subject — you still face the weighted lottery (~61% per draw at Level IV). Good if you win; have a cap-exempt backup on your list.

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Files H-1B transfers

2 transfer filings in the last year, covering 2 workers. Median labor-condition decision: 9 days. An employer that already files transfers is one that can take over an existing H-1B.

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About the role

  • We're looking for a Research Scientist to investigate how intervening on training data can improve the quality and shape the behavior of deep learning models.
  • The research literature is vast, rife with ambiguity, and constantly evolving.
  • Our research is guided by concrete customer needs and product outcomes, not conference benchmarks.

Responsibilities

  • You'll source and implement ideas from the literature, conduct research grounded in real customer needs, and collaborate closely with engineers and product teams to turn findings into tangible impact.
  • The research literature is vast, rife with ambiguity, and constantly evolving. You'll source, vet, implement, and improve promising ideas from the literature and your own thinking.
  • Enough software engineering and PyTorch experience (or willingness to learn) to run large-scale experiments and build production prototypes

Nice to have

  • Experience with distributed data processing tools like Spark or Snowflake
  • Candidates do not need a PhD or extensive publications.
  • We believe adaptability, combined with exceptional communication and collaboration skills, are the most important ingredients for successful research in a startup environment.
  • Starting pay is based on job-related skills, experience, qualifications, and interview performance.

Skills

  • Data pruning and curation
  • Curriculum learning
  • Synthetic data generation
  • Dataset distillation
  • Effects of training data on model behavior
  • Embedding models and semantic search
  • Training large vision (including video), language, or multimodal models
  • Efficient ML
  • A demonstrated track record in deep learning research, whether through papers, tools, or other artifacts

Compensation

  • At DatologyAI, we are dedicated to rewarding talent with competitive salary and meaningful equity. The salary for this position ranges from $180,000 to $300,000.
  • Starting pay is based on job-related skills, experience, qualifications, and interview performance.

Benefits

  • 100% covered health benefits (medical, vision, and dental).
  • Unlimited PTO policy
  • Paid Parental Leave of 12 weeks, plus 6 months of WFH flexibility.
  • Annual $2,000 wellness stipend.
  • Annual $1,000 learning and development stipend.
  • This role requires strong scientific judgment, fluency with the deep learning literature, and the drive to work autonomously in a fast-moving startup environment.
  • We expect Research Scientists to collaborate closely with engineers, talk to customers, and shape the product vision.

Company info

  • Models are what they eat.
  • But a large portion of training compute is wasted training on data that are already learned, irrelevant, or even harmful, leading to worse models that cost more to train and deploy.
  • At DatologyAI, we've built a state of the art data curation suite to automatically curate and optimize petabytes of data to create the best possible training data for your models.
  • For more details, check out our recent research on synthetic data scaling (BeyondWeb https://www.datologyai.com/blog/beyondweb) and pretraining with domain-specific data (The Finetuner's Fallacy https://www.datologyai.com/blog/finetuners-fallacy).
  • We raised a total of $57.5M in two rounds, a Seed and Series A.
  • This role is based in San Mateo, CA.
  • We are in office 4 days a week.
  • Training on curated data can dramatically reduce training time and cost (7-40x faster training depending on the use case), dramatically increase model performance as if you had trained on >10x more raw data without increasing the cost of training, and allow smaller models with fewer than half the parameters to outperform larger models despite using far less compute at inference time, substantially reducing the cost of deployment.
  • Our investors include Felicis Ventures, Radical Ventures, Amplify Partners, Microsoft, Amazon, and AI visionaries like Geoff Hinton, Yann LeCun, Jeff Dean, and many others who deeply understand the importance and difficulty of identifying and optimizing the best possible training data for models.
  • Our team has pioneered this frontier research area and has the deep expertise on both data research and data engineering necessary to solve this incredibly challenging problem and make data curation easy for anyone who wants to train their own model on their own data.
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