Founding Inference Systems Engineer

Touchdown Labs; United States; Remote; USD 140,000 - 210,000; Full time

Job summary

Touchdown Labs is seeking a role to own the systems path for AI inference workloads, guiding performance, reliability, and cost targets from request handling to runtime and hardware. You will translate measured results into deployable configurations and runbooks.

Responsibilities include defining workload distributions, measuring latency and throughput, and tracing bottlenecks across components. You will compare models, runtimes, routing, and hardware while building scalable serving paths and

Qualifications

• Strong systems fundamentals and experience diagnosing performance or reliability issues.
• Practical knowledge of model serving, concurrency, scheduling, memory, and distributed systems.
• Ability to build representative benchmarks and distinguish fixture results from live runtime evidence.
• Experience with profiling, telemetry, capacity analysis, and production investigations.
• Python plus a systems language, or deep specialization in a runtime.
• Ability to connect low-level measurements to user-visible quality and operating cost.

Responsibilities

• Define workload distributions, service objectives, quality constraints, and cost limits before tuning.
• Measure latency, throughput, queueing, memory and cache behavior, retries, and recovery.
• Compare models, providers, runtimes, routing, batching, quantization, and hardware under a consistent acceptance test.
• Trace bottlenecks across preprocessing, scheduling, networks, storage, accelerators, and memory movement.
• Build and operate appropriate serving paths, using systems such as vLLM, SGLang, LMCache, or TensorRT-LLM where they fit.
• Convert benchmark and trace findings into capacity plans, alerts, rollback thresholds, and runbooks.

Skills

Systems fundamentals

Performance troubleshooting

Model serving

Concurrency

Scheduling

Memory management

Distributed systems

Profiling

Telemetry

Capacity analysis

Python

Systems language

Runtime specialization

Tools

vLLM

SGLang

LMCache

TensorRT-LLM

Job description

Role overview

Own the systems path that serves AI inference workloads, from request and model behavior through runtime, hardware, and operating cost. You will use representative workloads and explicit quality targets to guide performance and reliability decisions, then turn measured results into deployable configurations and operating practices.

Responsibilities

• Define workload distributions, service objectives, quality constraints, and cost limits before tuning.
• Measure latency, throughput, queueing, memory and cache behavior, retries, and recovery.
• Compare models, providers, runtimes, routing, batching, quantization, and hardware under a consistent acceptance test.
• Trace bottlenecks across preprocessing, scheduling, networks, storage, accelerators, and memory movement.
• Build and operate appropriate serving paths, using systems such as vLLM, SGLang, LMCache, or TensorRT-LLM where they fit.
• Convert benchmark and trace findings into capacity plans, alerts, rollback thresholds, and runbooks.

Requirements

• Strong systems fundamentals and experience diagnosing performance or reliability issues.
• Practical knowledge of model serving, concurrency, scheduling, memory, and distributed systems.
• Ability to build representative benchmarks and distinguish fixture results from live runtime evidence.
• Experience with profiling, telemetry, capacity analysis, and production investigations.
• Python plus a systems language, or deep specialization in a runtime.
• Ability to connect low-level measurements to user-visible quality and operating cost.

Nice to have

GPU performance modeling; KV-cache or memory-tier systems; distributed inference and networking; quantization and numerical evaluation; or serving infrastructure and open-source work.