Senior Engineer II, Serverless Inference

About the role

We are seeking a Senior Engineer II to implement and contribute to the design and optimization of our Serverless Inference infrastructure and APIs. In this role, you will tackle the challenges of large-scale AI workloads, focusing on throughput, GPU utilization, and fault tolerance to support next-generation inference needs of AI native enterprises.

Responsibilities

• Design and build scalable, multi-tenant services that power AI inference and intelligent routing workloads.
• Strengthen platform resiliency through improved observability, capacity management, automation, and operational tooling.
• Partner closely with platform, GPU infrastructure, and product engineering teams to deliver production-grade systems and highly available APIs.
• Raise the engineering bar through strong software design, operational discipline, incident management, and continuous improvement practices.
• Contribute to architecture decisions around traffic management, service orchestration, reliability, and platform scalability.
• Participate in on-call rotations and lead efforts to reduce operator pain, improve service health, and prevent recurring incidents.
• Provide technical leadership on projects by breaking down ambiguous problems and guiding engineers toward clear execution plans.
• Support the growth of junior and mid-level engineers through coaching, pairing, and constructive feedback.
• Foster a culture of ownership, accountability, and continuous learning across the team.
• Lead by example in incident response, operational readiness, and production-quality engineering practices.

Requirements

• 7+ years of experience building and operating multi-tenant platforms or distributed backend systems
• Strong experience operating high-scale distributed services in production environments
• Deep understanding of SRE principles, including observability, incident management, reliability engineering, capacity planning, and operational automation
• 1+ years of hands-on experience with Go / Golang in production systems
• 2+ years of experience with Kubernetes
• Strong understanding of cloud-native architectures, microservices, and distributed systems fundamentals
• Experience debugging performance, scalability, and reliability issues in production systems
• Observability proficiency: experience tracking infrastructure and inference metrics like Time To First Token (TTFT), Time Per Output Token (TPOT), and GPU utilization

Qualifications

• Bonus AI/ML Framework Knowledge: understanding of modern LLM serving architectures and familiarity with engines like vLLM or Triton
• Experience with API gateways, traffic routing, or service mesh technologies
• Familiarity with LLM serving stacks such as vLLM, TensorRT-LLM, or similar technologies
• Experience building systems for inference optimization, rate limiting, routing, or workload orchestration

Benefits

• Career development resources and reimbursement for relevant conferences, training, and education
• Access to LinkedIn Learning's 10,000+ courses
• Employee Assistance Program
• Local Employee Meetups
• Flexible time off policy
• Competitive array of benefits supporting well‑being