# Funding Engineer

## Summary
- Organization: Auriko AI
- Location: San Francisco
- Type: Full-Time
- Department: N/A
- Status: active
- Posted: [object Object]
- Updated: [object Object]
- Closing Date: N/A
- External Apply: No
- External Apply URL: support@talentreview.ai

## Details
- Salary: N/A
- Experience: N/A
- Education: N/A
- Team: N/A
- Reporting To: N/A

## Description
## # About this Opportunity


### About Auriko

Auriko is building the market infrastructure for AI inference.


Our current product is an inference platform built with quantitative trading rigor. It enables AI teams to quickly switch models across inference providers and optimize inference cost.


Our team comes from quantitative trading and high-frequency derivatives backgrounds, with experience building, trading, and risk-managing complex systems at scale.


### Role

You will work with the quantitative researchers and own the production systems that serve the platform’s routing, pricing, and risk models.


### You would be responsible for:

Routing and optimization engine: productionize constrained optimization models that filter, score, and select providers across requests, subject to user constraints such as latency, cost, and uptime, as well as platform constraints such as capacity and rate limits

Predictive signal infrastructure: build the serving layer for quantitative performance and risk signals, including latency distributions, provider health forecasts, and pricing changes, that feed real-time routing decisions at the edge

Data pipeline: build and maintain quant-grade data collection, transformation, and serving infrastructure for platform measurements, including token usage, realized costs, TTFT, success and failure rates, and provider health signals, to specifications defined by the research team

Backtesting and simulation: build frameworks to evaluate routing strategies under stress scenarios, including provider degradation, price changes, capacity shocks, and partial outages

Metrics and validation: build aggregation logic for operational metrics such as P50/P95 latency, uptime, and error rates, data integrity validation, and holdout testing to confirm model improvements against defined targets

Provider integrations: build and maintain adapters across a growing set of LLM providers, each with its own API shape, streaming behavior, and failure modes

Billing and metering: build accurate usage tracking across providers with fundamentally different pricing models

SDKs: build Python and TypeScript clients that serve as drop-in replacements for existing provider SDKs


### Requirements

Production backend systems experience in Python and TypeScript

Experience productionizing quantitative, optimization, or ML models into low-latency serving systems

Comfort with numerical computing: you can read an optimization spec, implement it, debug it, and reason about edge cases

Distributed systems experience: multi-region services, consistency tradeoffs, and debugging under real load


### Strong Pluses

Background in quant finance, trading systems, or fintech infrastructure

Edge compute, including CDN-layer request handling and globally distributed systems

Billing or metering systems where precision is a hard requirement

SDK design and API ergonomics

Familiarity with LLM provider APIs such as OpenAI, Anthropic, Google, Mistral, etc.


We are building a small, high-agency team. We value ownership, intellectual honesty, independent thinking, and fast execution. We care about substance over appearance.


We believe in identifying high-agency, driven talent and incentivizing them to do exceptional work. **Our compensation package is market-leading, with meaningful equity upside.**


**Auriko sponsors work visa.
**


[Learn more
](www.auriko.ai)

Contact:
join@auriko.ai

## Responsibilities
None

## Skills
None

## Tags
None

## Organization
- Name: Auriko AI
- Website: www.auriko.ai
- Industry: Technology
- Size: 2-10
- Founded Year: 2026
- Description: Auriko is building the market infrastructure for AI inference.

Our current product is an inference platform built with quantitative trading rigor. It enables AI teams to quickly switch models across inference providers and optimize inference cost.

Our team comes from quantitative trading and high-frequency derivatives backgrounds, with experience building, trading, and risk-managing complex systems at scale.