Software Engineer, Infrastructure

DatologyAI; Philippines; Vor Ort; PHP 11.524.000 - 19.206.000; Vollzeit

Benefits dieser Stelle

Health benefits

401(k) match

Unlimited PTO

Parental Leave with WFH flexibility

Wellness stipend

Learning stipend

Lunch provided at office

Relocation assistance

Zusammenfassung

DatologyAI is hiring an experienced Infrastructure Engineer to join our core team in San Mateo, CA. You will shape the direction of our data infrastructure, support multi-cloud deployments, and influence deployment models for training and inference workloads.

You will lead development of scalable, reliable and secure systems, partnering with researchers and engineers to bring new capabilities to customers. This is a senior, hands-on role with broad impact on product and culture.

Qualifikationen

• 4+ years of relevant experience.
• Experience leading large-scale infrastructure projects.
• Proficiency in bash, Kubernetes, Python, and/or Terraform.
• Experience with AWS, Azure or GCP and/or on-prem environments.
• Ability to debug across the stack (networking, performance, memory).
• Own problems end-to-end and drive outcomes.

Aufgaben

• Design and build development and production platforms for reliability and security at scale.
• Architect and deploy core infrastructure across multi-cloud providers and deployment models.
• Empower engineers with tooling and systems for best-in-case experiences.
• Collaborate with researchers and engineers to deliver new features.

Kenntnisse

Bash

Kubernetes

Python

Troubleshooting

End-to-end ownership

Security

Tools

Terraform

AWS

GCP

Azure

Kubernetes

Networking

Jobbeschreibung

About The Company

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. 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. For more details, check out our recent research on synthetic data scaling (BeyondWeb) and pretraining with domain-specific data (The Finetuner's Fallacy).

We raised a total of $57.5M in two rounds, a Seed and Series A. 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.

This role is based in San Mateo, CA. We are in office 4 days a week.

About The Role

We're looking for an experienced Infrastructure Engineer to join as a member of our core Datology AI team. As one of our early senior hires, you will partner closely with our founders on the direction of our product and drive business-critical technical decisions. You will lead the development of our data infrastructure capabilities, including multi-cloud support and support for various deployment models, as well as training and inference infrastructure. You will have a broad impact on the technology, product, and our company's culture.

What You’ll Work On

• Design and build the development and production platforms that power our products, enabling reliability and security at scale
• Architect, build, and deploy our core infrastructure while supporting multiple cloud providers and various deployment models
• Accelerate company productivity by empowering your fellow engineers & teammates with excellent tooling and systems, providing a best-in-case experience
• Partner with researchers and engineers to bring new features and research capabilities to our customers

About You

• 4+ years of relevant experience
• Have meaningful experience in spearheading and constructing large-scale infrastructure
• Proficiency in bash, Kubernetes, Python, and/or Terraform or similar technologies
• Have experience working with AWS, other cloud platforms such as Azure or GCP and/or on-prem environments
• Have expertise in debugging problems across the stack, such as networking issues, performance problems, hardware issues or memory leaks
• Take pride in building and operating scalable, reliable, secure systems
• Have a humble attitude, an eagerness to help your colleagues, and a desire to do whatever it takes to make the team succeed
• Own problems end-to-end and are willing to pick up whatever knowledge you’re missing to get the job done

We Would Love It If You Had

• Built out data infrastructure from, or nearly from, scratch at a fast-growing startup.
• Experience building ML/DL infrastructure and/or data infrastructure that feeds into training large ML models

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).
• 401(k) plan with a generous 4% company match.
• 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.
• Daily lunches and snacks are provided in our office!
• Relocation assistance for employees moving to the Bay Area.