Datology AI

Software Engineer, Cloud Infrastructure

Redwood City

Sponsorship not specified$180k-$300kDetected 174 days ago

PythonBashAWSGCPAzureCloud PlatformsKubernetesTerraformCI/CDMachine LearningData EngineeringResearch

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.
401(k) plan with a generous 4% company match.

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 Redwood City, 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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