Applied AI Engineer

arxtalent.com•New York, NY

About The Position

We are a high-growth, venture-backed startup rethinking the recruitment landscape through an AI-native marketplace. Having recently secured a significant seed round from top-tier investors, we have achieved strong product-market fit and are scaling rapidly to meet massive market demand. Our business model leverages cross-sided network effects that compound as foundation models improve. We are currently a lean, high-output team looking for a "high-horsepower" engineer to help us increase capacity and navigate an expansive long-term roadmap. As one of the first engineers, you will own the applied AI systems that power our marketplace end-to-end. This is a builder-centric role focused on production-grade AI rather than theoretical research. You will partner directly with the founder and operations team to translate real-world feedback into scalable product features.

Requirements

4+ years of software engineering experience, with a proven track record of shipping AI/LLM features in a production environment.
End-to-end project ownership, from data pipeline design and model selection to deployment and iteration.
Deep experience with LLM APIs, embeddings, vector databases, and fine-tuning.
Strong foundations in backend web development and API design.

Nice To Haves

Experience in a founding team or an early-stage, high-growth startup.
Background in HR-tech or recruitment marketplaces.
Academic background from a top-tier technical institution (e.g., Stanford, MIT, CMU, Berkeley).

Responsibilities

Refine candidate-job matching algorithms to improve placement quality and speed.
Develop calibration tools that allow users to preview AI scoring logic.
Implement systems to analyze previous rejections and surface promising new candidates.
Build intelligent notification systems ranked by business value and fit.
Automate manual marketplace operations with intelligent workflows.
Create AI-powered flagging for candidates or submissions requiring manual intervention.
Develop feedback loops where the system learns from operational overrides to improve accuracy.
Generate automated candidate summaries based on resumes, notes, and transcripts.
Deploy retrieval-based assistants (RAG) to answer real-time questions about specific roles.
Build suggestion engines and one-click submission features with pre-filled data.
Design evaluation protocols to measure model impact in production environments.
Build internal infrastructure for offline evaluation and A/B testing.
Stay current with emerging AI methods, making pragmatic decisions on which technologies to adopt.
Collaborate on the long-term AI roadmap to balance rapid growth with technical durability.