Distributed Systems Engineer — AI Inference Platform

Baseten; New York (NY); On-site; USD 180,000 - 360,000; Full time

Benefits offered by this job

Competitive compensation with equity

Medical, dental, and vision coverage

Flexible PTO with Winter Break

Paid parental leave

Fertility and family-building stipend

401(k) plan

ML startup exposure

Job summary

Baseten is seeking distributed systems engineers to build the runtime powering large-scale LLM inference. You will ensure fast, reliable, and cost-efficient model deployments, working across the stack from developer tooling to Kubernetes orchestration and GPU workloads.

Join a small, high-impact team at Baseten, shaping how developers deploy AI models at scale. The role emphasizes ownership, observability, and collaboration with performance and infra teams to optimize customer outcomes.

Qualifications

• Bachelor's, master's, or Ph.D. in CS, engineering, or related field, or equivalent practical experience.
• 3+ years building and operating distributed systems, backend infra, or large-scale APIs with focus on reliability, latency, and scale.
• Proven track record owning low-latency, reliable backend services including rate limiting, auth, quotas, metering, and migrations.
• Infra instincts with performance focus: profiling, tracing, capacity planning, and SLO management.
• Comfort debugging performance and reliability across stack layers from app to runtime.
• Strong sense of developer experience and how systems are used.
• Eagerness to learn new languages, frameworks, and systems; ML/LLM experience not required but helpful.
• Excellent written communication and collaboration skills, including design docs and cross-functional work.

Responsibilities

• Build the infrastructure and orchestration systems that deploy and run large-scale distributed LLM inference, including routing, autoscaling, scheduling, and runtime management.
• Design, build, and operate Model APIs, with a focus on advanced inference capabilities: structured outputs (JSON mode, grammar-constrained generation), tool/function calling, and multimodal serving.
• Implement platform fundamentals such as API versioning, validation, usage metering, quotas, and authentication.
• Instrument deep observability (metrics, traces, logs) and build repeatable benchmarks for speed, reliability, and quality. Help set best practices for testing, release automation, and operational excellence.
• Debug and harden complex production systems spanning Kubernetes, distributed runtimes, networking, and GPU workloads to improve reliability and scalability.
• Partner with Inference Performance engineers and other teams to make new optimizations broadly available to customers and easy to configure.
• Own projects end to end, from architecture through deployment, monitoring, and iteration on customer feedback. Along the way, make thoughtful tradeoffs between performance, reliability, operational simplicity, and developer experience.

Skills

Distributed systems

Backend infrastructure

APIs

Performance profiling

Tracing

SLO management

Developer experience

Inference engineering

Model serving

Education

Bachelor's degree in CS/Engineering or related field

Tools

Kubernetes

Job description

Baseten is seeking distributed systems engineers to build the runtime powering large-scale LLM inference. You will ensure fast, reliable, and cost-efficient model deployments, working across the stack from developer tooling to Kubernetes orchestration and GPU workloads.

Join a small, high-impact team at Baseten, shaping how developers deploy AI models at scale. The role emphasizes ownership, observability, and collaboration with performance and infra teams to optimize customer outcomes.