Software Engineer, Applied AI

Auctor

New York (NY)

On-site

USD 175,000 - 290,000

Full time

14 days+

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Benefits offered by this job

Early-stage equity

Competitive, top-of-market salary

Catered lunch and dinners

Job summary

A leading AI services company is seeking a Software Engineer, Applied AI to design and improve core systems for their agent technology. This role involves building and enhancing systems across retrieval, document understanding, and orchestration. Strong Python skills and experience with LLM-powered products are essential. The position is based in New York and offers a competitive salary range of $175,000 to $290,000 plus equity, with benefits like catered meals and early-stage equity.

Qualifications

Strong engineering fundamentals and the ability to ship production systems.
Experience building or working on LLM-powered products, agent systems, or adjacent applied AI systems.
High ownership and comfort working in ambiguity.

Responsibilities

Build and improve the core systems behind our agents across retrieval and orchestration.
Design evals and experiments to understand agent quality in production.
Work closely with operations and deployed teams to understand workflows.

Skills

Fluency in Python

Strong engineering fundamentals

Experience with LLM-powered products

Empirical mindset

Strong systems taste

High ownership

Opinions on agent systems

Job description

Why Auctor

Auctor is building the AI layer for professional services and software implementation. Think of us as the brain behind the best solution engineers, forward‑deployed engineers, and onboarding teams—automating the documentation, the discovery, and the decision‑making that powers $400B+ in services work. We're going after one of the biggest software categories of the decade.

Role Overview

As a Software Engineer, Applied AI at Auctor, you will design, build, and improve the core systems behind our agents in production. This role sits at the boundary of engineering and empirical research. You will work across retrieval, document understanding, tool use, context management, prompting, and orchestration. Some weeks you will be shipping new capabilities; some weeks you will be mining production traces, designing evals, and figuring out which part of the system is actually failing. We are not looking for someone to glue an API onto a product and call it AI. We are looking for someone who wants to build real agent systems, understand how they behave in the wild, and use that understanding to make bold product and architecture decisions. This role is based in New York, NY, in person 5 days per week.

What You'll Do

Build and improve the core systems behind our agents across retrieval, tool use, document understanding, memory, and orchestration
Design evals and experiments that help us understand agent quality in production
Turn traces, failures, and user behavior into concrete product and architecture decisions
Work closely with operations, GTM, and deployed teams to understand real workflows and where agents break down
Evaluate models, prompts, and system designs across real enterprise tasks
Own the loop from idea → implementation → measurement → iteration

What We're Looking For

Strong engineering fundamentals and the ability to ship production systems
Fluency in Python
Experience building or working on LLM‑powered products, agent systems, or adjacent applied AI systems
An empirical mindset — you reach for logs, traces, experiments, and real usage before guessing
Strong systems taste — you understand that retrieval, prompting, memory, tools, and UX interact
High ownership and comfort working in ambiguity
Strong opinions about what makes agent systems actually work

Strong Candidates May Also Have

Experience with retrieval, search, or ranking systems
Experience designing evals, benchmarks, or feedback loops for LLM systems
Experience building internal tools, workflow products, or operator‑facing systems
Experience in startups or other high‑ownership environments

Example Projects

This is a new field. We care much more about what you have built than whether your background fits a standard template. Projects that would make us excited include:

Designing and shipping an agent harness that materially improved performance on a real task
Building an eval or benchmark that changed what your team decided to build next
Designing tool interfaces, memory systems, or retrieval systems for an LLM‑powered product
Building a production workflow around language models that users actually depended on
Running a careful experiment on prompting, model routing, or orchestration and using it to drive a product decision

If you apply, we would love to see one thing you built with LLMs or agents. It does not need to be perfect or flashy. We mostly want to understand how you think, what you owned, what you learned, and what tradeoffs you made.

Compensation

$175,000–$290,000 base salary, plus equity.

Benefits