Distributed Systems Engineer — AI Inference Platform

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

Benefits offered by this job

Competitive compensation with equity

100% healthcare coverage for employee

Flexible PTO including Winter Break

Paid parental leave

Fertility and family-building stipend

401(k) (U.S. only)

Exposure to ML startups for learning

Job summary

Baseten is hiring distributed systems engineers to build the runtime powering large-scale LLM inference. You’ll deploy and operate APIs and runtimes, focusing on performance, reliability, and cost efficiency across Kubernetes-based deployments.

You’ll own end-to-end projects, drive architectural decisions, and collaborate with performance engineers to deliver scalable solutions for modern AI workloads while enhancing developer experience and platform usability.

Qualifications

• Bachelor's, Master's, or Ph.D. in Computer Science, Engineering, or a related field, or equivalent practical experience.
• 3+ years building and operating distributed systems, backend infrastructure, or large-scale APIs where reliability, latency, and scale are first-class concerns.
• A proven track record of owning low-latency, reliable backend services, including rate limiting, auth, quotas, metering, and migrations.
• Infrastructure instincts with a feel for performance: profiling, tracing, capacity planning, and SLO management.
• Comfort debugging performance and reliability issues across multiple layers of the stack, from application behavior down to runtime and infrastructure internals.
• A strong sense of developer experience. You think about how systems are used, not just how they work.
• Eagerness to learn new languages, frameworks, and systems, and a real interest in inference engineering. Prior ML or LLM experience isn't required, though experience with model serving or inference systems is a plus.
• Excellent written communication and collaboration skills, including writing clear design docs and working across functions.

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

Developer experience

Observability

Education

Bachelor's/Master's/Ph.D. in CS or Engineering

Tools

Kubernetes

API design

Job description

Baseten is hiring distributed systems engineers to build the runtime powering large-scale LLM inference. You’ll deploy and operate APIs and runtimes, focusing on performance, reliability, and cost efficiency across Kubernetes-based deployments.

You’ll own end-to-end projects, drive architectural decisions, and collaborate with performance engineers to deliver scalable solutions for modern AI workloads while enhancing developer experience and platform usability.