Forward Deployed AI Engineer, Post-Sales

Forward Deployed AI Engineer, Post-Sales

Company: DatologyAI
Location: Redwood City, CA (4 days in office; travel to customer sites as needed)
Compensation: $230,000 - $300,000 + competitive equity
Employment Type: Full-time
Visa Sponsorship: Visa transfers (OPT, H-1B transfer); relocation considered

About DatologyAI

DatologyAI is building the future of AI training data on a simple insight: models are what they eat. Much training compute is wasted on data that is already learned, irrelevant or harmful. DatologyAI's data curation suite automatically curates and optimizes petabytes of data to produce the best possible training data for deep learning models.

DatologyAI has raised a $57.5M Series A.

The Role

DatologyAI is hiring a Forward Deployed AI Engineer (5+ years) to be the trusted technical advisor for its most strategic customers, guiding complex deployments of its data curation platform. You will thrive in ambiguity, enjoy distributed systems challenges, and be as comfortable in a customer boardroom as deep in infrastructure configs.

What You Will Do

• Own onboarding, deployment and production rollout for strategic accounts.
• Serve as primary technical contact and drive adoption across complex on-prem and hybrid environments.
• Design scalable, secure workflows across compute, storage, networking and distributed systems on AWS, GCP, Azure and on-prem Kubernetes.
• Turn customer requirements into technical strategy with Sales, Engineering and Research, and feed back into the roadmap.
• Travel to customer sites for critical deployments.

What You Bring

• 5+ years in a post-sales technical role (solutions, customer or implementation engineering)
• Hands-on distributed systems, data infrastructure and on-prem or hybrid compute experience
• Deep multi-cloud expertise across AWS, GCP and Azure (compute, storage, networking, IAM)
• Experience with ML/AI workflows, Kubernetes, data pipelines or large-scale backend infrastructure
• Proficiency in Python or SQL

Tech Stack

Python, SQL, Kubernetes, AWS, GCP, Azure, distributed systems, on-prem infrastructure, ML/AI workflows, data pipelines, infrastructure as code