Build developer-friendly APIs, SDKs, and CLIs that let researchers and engineers—experts and non-experts alike—submit and manage batch inference jobs with minimal effort, particularly in the domain of content and media
Design, implement, and operate distributed services that package, schedule, execute, and monitor batch inference workflows at massive scale.
Instrument the platform for reliability, debuggability, observability, and cost control; define SLOs and share an equitable on-call rotation.
Foster a culture of engineering excellence through design reviews, mentorship, and candid, constructive feedback.

Requirements

Hands-on experience with ML engineering or production systems involving training or inference of deep-learning models.
Proven track record of operating scalable infrastructure for ML workloads (batch or online).
Proficiency in one or more modern backend languages (e.g. Python, Java, Scala).
Production experience with containerization & orchestration (Docker, Kubernetes, ECS, etc.) and at least one major cloud provider (AWS preferred).
Comfortable with ambiguity and working across multiple layers of the tech stack to execute on both 0-to-1 and 1-to-100 projects.
Commitment to operational best practices—observability, logging, incident response, and on-call excellence.
Excellent written and verbal communication skills; effective collaboration across distributed teams and time zones.
Comfortable working in a team with peers and partners distributed across (US) geographies & time zones.

Benefits

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Applicant Tracking System Keywords

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Hard Skills & Tools

ML engineeringdeep-learning modelsscalable infrastructurebackend languagesPythonJavaScalacontainerizationorchestrationcloud provider

Soft Skills

engineering excellencementorshipconstructive feedbackcommunication skillscollaborationcomfort with ambiguityteamwork