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