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About This Role
What if the work you did every day could impact the lives of people you know? Or all of humanity?
At Illumina, we are expanding access to genomic technology to realize health equity for billions of people around the world. Our efforts enable life\-changing discoveries that are transforming human health through the early detection and diagnosis of diseases and new treatment options for patients.
Working at Illumina means being part of something bigger than yourself. Every person, in every role, has the opportunity to make a difference. Surrounded by extraordinary people, inspiring leaders, and world changing projects, you will do more and become more than you ever thought possible.Location
This role is located at our HQ in San Diego, CA.
Position Summary
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.
Key Responsibilities
Technical Leadership
- 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.
AI Systems Development
- 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.
Platform and Infrastructure
- 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.
Research to Production
- 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.
Cross\-Functional Influence
- 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.
Required Qualifications
- 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.
Preferred Qualifications
- 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.
Technical Skill Profile
Foundation Models and LLMs: GPT\-4 class models, Claude, Gemini, open\-weight models (Llama, Mistral, Qwen), fine\-tuning techniques, instruction tuning, alignment methods.
AI Frameworks and Tooling: PyTorch, Hugging Face Transformers, LangChain, LangGraph, LlamaIndex, DSPy, Ray, vLLM, TensorRT\-LLM, model serving frameworks.
RAG and Retrieval: Vector databases (Pinecone, Weaviate, pgvector, Vertex AI Vector Search), embedding models, reranking, hybrid search, chunking strategies, query understanding.
Evaluation and Observability: RAGAS, DeepEval, custom eval harnesses, LangSmith, Weights and Biases, Arize, OpenTelemetry for AI workloads.
Programming and Engineering: Python (expert), one additional systems language (Go, Java, or Rust), SQL, distributed systems, microservices, API design.
Cloud and Platform: GCP (Vertex AI, AlloyDB, GKE), Azure (Azure AI Foundry, AKS), AWS (Bedrock, SageMaker, EKS), Docker, Kubernetes, Terraform.
Data: Streaming and batch pipelines, lakehouse architectures, feature stores, data versioning (DVC, LakeFS), high\-throughput ETL.
Engineering Competencies
- Technical Depth: Expert\-level mastery of AI engineering with the ability to operate from research papers down to production code.
- Systems Thinking: Comfort designing systems that span multiple services, data stores, model providers, and failure modes.
- Pragmatism: Strong instinct for when to build, when to buy, and when to wait, with a track record of avoiding over\-engineering.
- Communication: Ability to explain complex AI concepts to executives, write design docs that drive decisions, and influence peers across disciplines.
- Builder's Mindset: Genuine enjoyment of writing code and solving hard technical problems, not just reviewing or directing others.
- Curiosity and Continuous Learning: Active engagement with the AI research landscape and a habit of trying new things.
The estimated base salary range for the Associate Principal, AI Engineer role based in the United States of America is: $187,400 \- $281,000\. Should the level or location of the role change during the hiring process, the applicable base pay range may be updated accordingly. The range reflects long‑term growth in the role; therefore, most candidates are hired between the minimum and middle of the range. Placement depends on experience, skills, location, and internal equity. Additionally, all employees are eligible for one of our variable cash programs (bonus or commission) and eligible roles may receive equity as part of the compensation package. We offer a wide range of benefits as innovative as our work, including access to genomics sequencing, family planning, health/dental/vision, retirement benefits, and paid time off.
We are a company deeply rooted in belonging, promoting an inclusive environment where employees feel valued and empowered to contribute to our mission. Built on a strong foundation, Illumina has always prioritized openness, collaboration, and seeking alternative perspectives to propel innovation in genomics. We are proud to confirm a zero\-net gap in pay, regardless of gender, ethnicity, or race. We also have several Employee Resource Groups (ERG) that deliver career development experiences, increase cultural awareness, and offer opportunities to engage in social responsibility. We are proud to be an equal opportunity employer committed to providing employment opportunity regardless of sex, race, creed, color, gender, religion, marital status, domestic partner status, age, national origin or ancestry, physical or mental disability, medical condition, sexual orientation, pregnancy, military or veteran status, citizenship status, and genetic information. Illumina conducts background checks on applicants for whom a conditional offer of employment has been made. Qualified applicants with arrest or conviction records will be considered for employment in accordance with applicable local, state, and federal laws. Background check results may potentially result in the withdrawal of a conditional offer of employment. The background check process and any decisions made as a result shall be made in accordance with all applicable local, state, and federal laws. Illumina prohibits the use of generative artificial intelligence (AI) in the application and interview process. If you require accommodation to complete the application or interview process, please contact [email protected]. To learn more, visit: https://www.dol.gov/ofccp/regs/compliance/posters/pdf/eeopost.pdf. The position will be posted until a final candidate is selected or the requisition has a sufficient number of qualified applicants.
Salary Context
This $187K-$281K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $181K across 1996 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Illumina, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($234K) sits 31% above the category median. Disclosed range: $187K to $281K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Illumina AI Hiring
Illumina has 4 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Based in San Diego, CA, US. Compensation range: $123K - $281K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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