Datology AI

Datology AI

Software Engineer, Cloud Infrastructure

Redwood City · Mid

Sponsorship not specified$180k-$300kDetected 261 days ago
PythonBashAWSGCPAzureCloud PlatformsKubernetesTerraformCI/CDMachine LearningData EngineeringResearch

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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 an experienced Cloud Infrastructure Engineer to join our core team at DatologyAI.
  • This role is a key early hire and offers an opportunity to have a deep technical and cultural impact.
  • Respond to and resolve infrastructure-related incidents with a sense of ownership and urgency

Responsibilities

  • Design and manage Kubernetes-based systems for model training, inference, and data processing workloads
  • Build monitoring, alerting, and logging systems to ensure high system availability and observability
  • Collaborate with research and engineering teams to provide infrastructure support for training large-scale ML models
  • Drive cost-efficiency strategies across compute and storage resources
  • You've led or helped build robust infrastructure systems at a startup or fast-moving engineering organization
  • You're collaborative, humble, and ready to own high-impact projects end-to-end

Nice to have

  • Experience supporting infrastructure for ML workloads (training pipelines, inference clusters, GPU orchestration)
  • Built or scaled infrastructure for teams working with large-scale datasets
  • Exposure to cost monitoring and optimization tools in cloud environments
  • Background supporting compliance and security in enterprise deployments
  • Starting pay is based on job-related skills, experience, qualifications, and interview performance.
  • 4+ years of relevant experience
  • Strong with Kubernetes, Terraform, and containerized architectures
  • Confident with systems-level debugging-networking issues, memory leaks, resource bottlenecks, etc.

Compensation

  • 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.

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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This listing is sourced directly from Datology AI's careers page and normalized into a canonical job model.