AI Engineer

Illumina•San Diego, CA

1d•$82,500 - $123,700•Onsite

About The Position

The AI Engineer is an early-career role for engineers who are passionate about building with modern AI and ready to grow into the next generation of AI specialists. You will work alongside senior and principal engineers to build, ship, and improve production AI features, learning the craft of applied AI engineering on real systems that serve real users. This role is hands-on from day one. You will write code, prototype ideas, evaluate models, integrate APIs, and contribute to the platform that powers AI across the organization. You will be supported with strong mentorship, clear scope, and the room to grow into more senior responsibilities over time.

Requirements

1 to 2 years of professional software engineering experience (internships, co-ops, and substantial open source contributions count).
Strong programming skills in Python, with familiarity in writing modular, testable code.
Working knowledge of how large language models behave in practice, including experience calling LLM APIs (OpenAI, Anthropic, Google, or open-weight models) in at least one project.
Familiarity with at least one of the following: RAG systems, prompt engineering, vector databases, embeddings, or basic agent patterns.
Solid foundation in software engineering basics including Git, REST APIs, JSON, SQL, and at least one cloud environment.
Strong written and verbal communication skills with a willingness to ask questions and engage in technical discussion.
Bachelor's degree in Computer Science, Data Science, Machine Learning, Engineering, or a related field, or equivalent demonstrable experience.

Nice To Haves

Experience with at least one AI framework such as LangChain, LlamaIndex, Hugging Face Transformers, or DSPy.
Exposure to vector databases (Pinecone, Weaviate, pgvector, Vertex AI Vector Search) and embedding models.
Familiarity with one major cloud platform (GCP, Azure, or AWS), particularly the managed AI services.
Comfort with Docker, basic CI/CD workflows, and modern engineering practices.
A portfolio of personal projects, open source contributions, hackathon work, or coursework that demonstrates curiosity and initiative in AI.
Experience with web frameworks (FastAPI, Flask) or frontend basics (React, TypeScript) is a plus but not required.
Coursework or self-directed learning in machine learning, deep learning, NLP, or information retrieval.

Responsibilities

Build and Ship Implement features in production AI applications, including LLM integrations, prompt workflows, retrieval pipelines, and supporting backend services.
Develop and maintain components of RAG systems, including data ingestion, chunking, embedding generation, and retrieval logic.
Write clean, tested, well-documented Python code that meets team standards for quality and maintainability.
Build internal tools, scripts, and prototypes that accelerate the team's ability to experiment and iterate.
Evaluate and Improve Run experiments to evaluate model performance, prompt variations, retrieval strategies, and end-to-end system behavior.
Develop and maintain evaluation datasets, test cases, and regression checks for AI features.
Analyze production logs and metrics to identify quality issues, latency bottlenecks, and cost optimization opportunities.
Contribute to incident response and root-cause analysis for AI system issues.
Learn and Contribute Stay current with the AI ecosystem by following research, exploring new tools, and bringing useful ideas back to the team.
Participate actively in code reviews, design discussions, and team rituals, asking questions and offering perspectives.
Document your work clearly so that teammates can build on it and learn from it.
Pair with senior engineers on complex problems and gradually take on larger scope as you grow.
Collaborate Work closely with product managers, designers, and other engineers to understand requirements and ship features that solve real user problems.
Communicate progress, blockers, and trade-offs clearly in standups, written updates, and design documents.
Support other teams by answering questions about AI capabilities and limitations.