Staff Software Engineer, AI Inference Gateway

Salad Technologies

United States

Remote

USD 180,000 - 220,000

Full time

14 days+

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Benefits offered by this job

Unlimited PTO

75% health premiums covered

Dental and vision coverage

401(k) plan

Stock options

Company-provided computer

$500 WFH budget

Fully remote, flexible hours

Job summary

Salad Technologies, fully remote and offering a challenging AI gateway role, seeks a Staff Software Engineer to own the AI Gateway system end to end. You will design, optimize, and scale real-time and batch inference across a fleet of GPU-backed nodes, making critical decisions on capacity, cost, and reliability.

You’ll work with a small expert team, own observability and billing telemetry, and benchmark new models and hardware.

Qualifications

Strong production Rust experience, ideally on high-throughput networked services.
Hands-on experience running LLM inference servers (vLLM, llama.cpp, TGI, TensorRT-LLM, or similar).
Distributed systems fundamentals: load balancing, backpressure, failure handling, unreliable nodes.
Comfort operating what you build: metrics, tracing, on-call instincts.
Willing to be on-call for a system you helped make quiet — we treat after-hours pages as a design bug to fix, not a lifestyle.
Clear written and verbal communication; you can run a design without hand-holding and bring others along.

Responsibilities

Own the AI Gateway system end to end: request routing, streaming, batch/async job handling, and horizontal scaling across multiple gateway servers.
Build and tune the fleet-efficiency algorithms — autoscaling nodes, scoring performance, and evicting underperformers so the network converges on peak throughput per dollar.
Operate and scale the underlying inference nodes running vLLM, llama.cpp, and similar servers.
Design and maintain OpenTelemetry-based observability across the gateway and the fleet; pay-per-token and subscription billing already rides on this telemetry, so you’ll keep that integration accurate.
Spend roughly 25% of your time benchmarking new models, quantizations, and GPU hardware, and work with the product and marketing teams to make the call on what goes into production.
Collaborate on cross-system design and integrations with the rest of Salad engineering; explain trade-offs clearly to both technical and non-technical teammates.

Skills

Rust

LLM inference

Distributed systems

Observability

On-call

Communication

Tools

vLLM

llama.cpp

TensorRT-LLM

Pingora

Tokio

Job description

Our mission is to democratize cloud computing

by utilizing latent consumer resources to power a sustainable, affordable, environmentally friendly cloud for everyone.

salad.com/about

Staff Software Engineer, AI Inference Gateway

Salad Technologies · Remote · Full-time

The role

SaladCloud runs inference on thousands of geodistributed workstation and consumer GPUs — NVIDIA and AMD — that nobody else can use. Sitting in front of that fleet is a unique AI Gateway written in Rust, built on Pingora and WireGuard, that serves real-time and batch inference and embeddings to customers as a single, reliable API. You’ll own it.

This is an infrastructure role with real autonomy. You’ll design and ship the systems that decide how requests are fulfilled, when to add or drop capacity, and how to bill for every token — and you’ll be the person who knows whether a new model, quantization, or GPU is actually worth running.

What you’ll do

Own the AI Gateway system end to end: request routing, streaming, batch/async job handling, and horizontal scaling across multiple gateway servers

Build and tune the fleet-efficiency algorithms — autoscaling nodes, scoring performance, and evicting underperformers so the network converges on peak throughput per dollar

Operate and scale the underlying inference nodes running vLLM, llama.cpp, and similar servers

Design and maintain OpenTelemetry-based observability across the gateway and the fleet; pay-per-token and subscription billing already rides on this telemetry, so you’ll keep that integration accurate

Spend roughly 25% of your time benchmarking new models, quantizations, and GPU hardware, and work with the product and marketing teams to make the call on what goes into production

Collaborate on cross-system design and integrations with the rest of Salad engineering; explain trade-offs clearly to both technical and non-technical teammates

The team

You’ll join a small team — five engineers plus the CTO, who still writes code, and two deeply technical product managers who ship their own fixes rather than queue them for you. You’ll report directly to the CTO. Everyone on the engineering team has 10+ years of experience and has built systems that already run at massive scale — with minimal incidents in a typical year, because we ship things that are production-worthy the first time.

You’ll be the first hire with inference-gateway expertise and you’ll be asked to own the whole surface. You won’t do it alone: your teammates are eager to learn the domain, will pair on design and integrations, and share after-hours support. The bar is high; so is the support.

We’ve listed a lot below. If you’re strong in two of the first three — Rust, LLM inference servers, distributed systems — apply, and expect to learn the rest fast with people who’ll help.

What we’re looking for

Strong production Rust experience, ideally on high-throughput networked services

Hands‑on experience running LLM inference servers (vLLM, llama.cpp, TGI, TensorRT‑LLM, or similar) — you know what a KV cache is and why it fills up

Distributed systems fundamentals: load balancing, backpressure, failure handling, unreliable nodes

Comfort operating what you build: metrics, tracing, on‑call instincts

Willing to be on‑call for a system you helped make quiet — we treat after‑hours pages as a design bug to fix, not a lifestyle

Clear written and verbal communication; you can run a design without hand‑holding and bring others along

Nice to have

Pingora, Tokio, or proxy/gateway internals

Experience with heterogeneous or consumer‑grade GPU fleets, CUDA, or ROCm

Quantization formats (GGUF, AWQ, GPTQ, FP8) and their performance characteristics

Why Salad

You’ll work on a hard, unusual problem — coaxing datacenter‑grade reliability out of hardware in people's homes — with the freedom to make major decisions and see them hit production quickly.

We’re scaling rapidly: demand for inference on SaladCloud currently exceeds the capacity we can bring online, and this role exists to keep up with it. There is no shortage of work and no prospect of it ramping down.

The platform handles workload security and isolation, so you can focus on performance. We don’t log or train on our customers’ prompts or data.

Benefits