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Applied AI Inference Engineer

Job Overview

Location

San Francisco

Job Type

Full-time

Category

Software Engineering

Date Posted

April 22, 2026

Full Job Description

📋 Description

  • As an Applied AI Inference Engineer at Baseten, you will partner directly with customers to architect, build, and deploy high-scale production AI applications on Baseten’s platform, translating ambiguous business goals into reliable, observable services with clear quality, latency, and cost outcomes.
  • You will develop and maintain software systems using general-purpose programming languages (with preference for Python), drive customer impact by designing and deploying end-to-end solutions, optimize AI/ML projects, and own products and customer projects from problem framing to production deployment and monitoring.
  • Baseten powers mission-critical inference for leading AI companies like Cursor, Notion, and OpenEvidence, uniting applied AI research, flexible infrastructure, and seamless developer tooling to enable cutting-edge models in production, backed by a $300M Series E from top-tier investors.
  • You will gain hands-on experience across product, software development, performance engineering, and customer-facing implementations, functioning as an engineer, project manager, and product manager while working with frontier AI companies and contributing to a rapidly growing platform shaping the future of AI deployment.

Skills & Technologies

Python
Docker
Apache Spark
Onsite
Degree Required

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BaseTen Inc.
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About BaseTen Inc.

BaseTen provides a serverless, GPU-accelerated platform that lets machine-learning teams deploy, scale and monitor custom models behind autoscaling inference endpoints. The service abstracts infrastructure management, supports PyTorch, TensorFlow and Hugging Face artifacts, and offers built-in observability, A/B testing and fine-tuning. Customers integrate via REST or GraphQL APIs and pay only for compute used. Founded in 2019 and headquartered in San Francisco, BaseTen targets data scientists and product teams seeking production-grade ML serving without Kubernetes complexity.

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