About the role

What we're looking for:

We need a senior or staff-level engineer with 4+ years of experience in cloud infrastructure who is equally strong at hands-on coding and systems design. You should be comfortable working in a small, fast-moving team with high ownership and minimal red tape, and have a track record of building and operating scalable, reliable cloud infrastructure. You must have a CS or engineering degree and be proficient in Python — this is a Python-heavy environment.

What you'll do:

Architect and maintain multi-cloud infrastructure (AWS primary, plus GCP, Azure, OCI, on-prem) to support customer deployments across diverse environments
Define and implement infrastructure-as-code using Terraform or Pulumi, driving best practices across the team
Design and manage Kubernetes-based systems for model training, inference, and data processing workloads
Write production-quality Python code daily — this is not a scripting-only role; you'll pass rigorous coding interviews and build real systems
Optimize CI/CD pipelines and streamline deployment of services across customer environments
Build monitoring, alerting, and logging systems to ensure high availability and observability
Collaborate closely with research and engineering teams to provide infrastructure support for training large-scale ML models
Drive cost-efficiency strategies across compute and storage resources
Respond to and resolve infrastructure incidents with ownership and urgency

About the team:

You'll join a cloud infrastructure team that's currently a team of one, reporting to the VP of Engineering (Paul Burstein). The broader engineering team is ~20 people. This is a high-impact, early hire with the opportunity to shape infrastructure decisions and grow with the team. Everyone holds the title "Member of Technical Staff" internally — the team is flat and collaborative with no rigid leveling yet.

DatologyAI is building the future of AI training data. The company's core insight is simple but powerful: models are what they eat. A large portion of training compute is wasted on data that is already learned, irrelevant, or even harmful — leading to worse models that cost more to train and deploy.

DatologyAI has built a state-of-the-art data curation suite that automatically curates and optimizes petabytes of data to create the best possible training data for deep learning models. Their algorithms are modality-agnostic (not limited to text or images) and don't require labels, making them ideal for powering the next generation of large-scale AI models.

The results are dramatic:

7x–40x faster training depending on the use case
Model performance improvements as if trained on >10x more raw data without increasing training cost
Smaller models with fewer than half the parameters outperforming larger models, substantially reducing deployment costs

Founded in 2023, DatologyAI raised a total of $57.5M across a Seed and Series A round. Their 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 of optimizing training data for models.

The company is headquartered in Redwood City, California, operates in-office 4 days a week, and is rapidly scaling — growing from approximately 54 people to 80–90 by end of year as they close out a strong customer pipeline. Their engineering team is approximately 20 people with a flat organizational structure, offering early employees massive ownership and the opportunity to shape how infrastructure and products are built at a critical inflection point in AI.

Senior Software Engineer, Cloud Infrastructure

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