Jobright is a next-generation AI job search platform built to make career navigation faster, smarter, and more personal. They are looking for an Applied AI Engineer to bridge the gap between state-of-the-art models and real product experiences, building the AI agents that help millions of people land better careers.

Why Join Us

Build AI features that ship to real users, not prototypes that live in notebooks See your work measurably improve agent performance, with tight feedback loops between experiments and production Grow alongside engineers and researchers who care deeply about getting LLM systems to actually work in the wild Join a team where applied AI is treated as core craft, with real investment in evaluation, infrastructure, and iteration speed

Responsibilities

Develop and deploy LLM-powered agents that handle resume parsing, job matching, interview prep, and other core experiences on the Jobright platform Experiment with retrieval strategies, prompt patterns, fine-tuning approaches, and tool-calling designs to push agent reliability and quality forward Partner with product and engineering teammates to take loosely defined problems and turn them into shipped AI features end to end Build evaluation harnesses, monitor live agent behavior, and use those signals to inform what to improve next

Qualifications

Required

Recent grad or early-career engineer with 0 to 2 years of experience in software engineering, machine learning, or a closely related technical field Clear communicator who can walk both technical and non-technical teammates through model tradeoffs, evaluation results, and design choices Working understanding of modern AI/ML systems, including LLMs, APIs, embeddings, and how agentic architectures fit together

Preferred

Internship or project experience in applied AI, ML engineering, or LLM product work at a tech or AI-focused organization Track record of shipping AI features in ambiguous, fast-moving settings where model capabilities and product needs shift week to week Hands-on skills in Python, familiarity with frameworks like LangChain or LlamaIndex, exposure to retrieval-augmented generation, prompt engineering, and SQL-based data tooling