Datology AI

Datology AI

Research Engineer

Redwood City · Mid

Sponsorship not specified$180k-$300kDetected 134 days ago
PythonMachine LearningSparkData EngineeringA/B TestingResearch

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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

  • As a Research Engineer, you will play a crucial role in conducting and enabling cutting-edge research and translating it into our core product pipeline.
  • Our work is guided by concrete customer needs and product outcomes.

Responsibilities

  • You will work closely with other members of the technical staff to develop and improve state-of-the-art data curation strategies.
  • You'll design the experimentation infrastructure that lets scientists iterate quickly at scale, turning a good idea into a running experiment in hours.

Requirements

  • Strong software engineering fundamentals, fluency in Python, and hands-on experience with PyTorch.
  • Experience with distributed data processing and/or distributed training, using tools such as Spark, Ray, Dask, or Snowflake.
  • A demonstrated track record of shipping systems that others rely on, whether through production infrastructure, open-source tools, or other artifacts.

Skills

  • Your technical skills will accelerate our research and ensure that our product remains at the forefront of innovation.
  • Comfort operating large-scale compute, including GPU clusters and cloud infrastructure.
  • Starting pay is based on job-related skills, experience, qualifications, and interview performance.

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.
  • At DatologyAI, we are dedicated to rewarding talent with competitive salary and meaningful equity.
  • You'll build and scale the data processing and curation pipelines that operate over massive language, vision, and multimodal datasets, and make them fast, reliable, and cheap to run.
  • Enough machine learning depth to collaborate substantively with researchers, not just implement their specs.

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.
  • Our work is guided by concrete customer needs and product outcomes. You'll take research results and harden them into production-grade systems that customers depend on.
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