Associate Principal, AI Engineer

Illumina•San Diego, CA

1d•$187,400 - $281,000•Onsite

About The Position

The Assoc Principal AI Engineer is the most senior individual contributor on the AI Engineering team, responsible for the design, development, and productionization of the most complex AI systems in the organization. This is a deeply technical, hands-on role for an engineer who has spent years in the trenches building, training, fine-tuning, and shipping AI systems at scale and is now ready to set technical direction across multiple teams. The role combines applied research with production engineering. The Assoc Principal AI Engineer translates the latest advances in foundation models, agentic systems, and machine learning into robust, observable, and economically viable production systems. They write code, design systems, lead the hardest technical decisions, and shape the engineering culture that determines how AI gets built across the company.

Requirements

12+ years of software engineering experience, with 6+ years focused on machine learning or AI systems and 2+ years building production Generative AI applications.
Demonstrated ownership of large-scale AI systems in production, including responsibility for latency, cost, accuracy, and reliability outcomes.
Deep hands-on expertise in Python and modern ML frameworks (PyTorch, TensorFlow, JAX, Hugging Face Transformers).
Strong command of LLM application development, including RAG architectures, prompt engineering, function calling, structured outputs, and agentic patterns.
Experience with model fine-tuning, evaluation, and deployment lifecycles across at least one major cloud platform (GCP, Azure, or AWS).
Proven ability to design distributed systems, including familiarity with vector databases, message queues, container orchestration, and observability stacks.
Bachelor's or Master's degree in Computer Science, Machine Learning, Statistics, or a related quantitative discipline.
PhD welcomed but not required.

Nice To Haves

Experience training, fine-tuning, or post-training foundation models using techniques such as SFT, DPO, RLHF, RLAIF, or constitutional methods.
Familiarity with agentic frameworks (LangChain, LangGraph, AutoGen, CrewAI, custom orchestration) and multi-agent system design patterns.
Background in Voice AI, speech systems, multimodal models, or computer vision applied at production scale.
Contributions to open source AI projects, peer-reviewed publications, or notable conference presentations.
Experience in regulated or high-stakes domains (life sciences, healthcare, financial services) where accuracy, safety, and governance requirements are stringent.
Familiarity with responsible AI practices including red-teaming, jailbreak resistance, content safety, bias evaluation, and AI governance frameworks.
Typically requires a minimum of 15 years of related experience with a Bachelor’s degree; or 12 years and a Master’s degree; or a PhD with 8 years experience; or equivalent experience.

Responsibilities

Set the technical direction for AI Engineering across foundation model integration, fine-tuning pipelines, RAG systems, agentic workflows, and evaluation infrastructure.
Own the most complex and ambiguous AI engineering problems in the company, from initial design through production deployment and ongoing optimization.
Establish engineering standards for model development, prompt management, evaluation, deployment, and observability that the rest of the AI organization adopts.
Lead architecture reviews and serve as the senior technical reviewer for high-stakes AI initiatives.
Design and build production-grade Generative AI systems including retrieval-augmented generation, multi-agent orchestration, tool-using agents, and domain-adapted models.
Develop fine-tuning, distillation, and post-training pipelines using techniques such as SFT, DPO, RLHF, and parameter-efficient methods (LoRA, QLoRA, adapters).
Architect and implement vector retrieval systems, semantic search, and hybrid retrieval pipelines optimized for accuracy, latency, and cost.
Build robust evaluation frameworks covering automated metrics, LLM-as-judge, human review, regression testing, and safety evaluations.
Design and build the AI platform that powers internal teams, including model serving infrastructure, prompt and prompt-template management, experiment tracking, and feature stores.
Optimize inference performance across latency, throughput, and cost, including quantization, batching, caching, speculative decoding, and intelligent routing across model providers.
Establish LLMOps practices for continuous evaluation, drift detection, prompt versioning, rollback strategies, and incident response.
Partner with platform and infrastructure teams to ensure AI workloads run reliably on GPU and accelerator hardware across cloud environments.
Stay current with the rapidly evolving AI research landscape and identify which advances translate into production value for the business.
Prototype emerging techniques (new model architectures, training methods, agent frameworks) and lead the path from experiment to production system.
Contribute to internal technical strategy on build versus buy decisions for foundation models, vector databases, agent frameworks, and AI tooling.
Partner with product, data science, research, and business stakeholders to scope AI initiatives and shape solutions that deliver measurable business impact.
Mentor senior and staff engineers, raising the technical bar across the AI organization.
Represent AI Engineering in executive forums, customer conversations, vendor evaluations, and industry engagements.
Author technical documents, design docs, and (where appropriate) external publications that contribute to the broader AI community.