Interested in this AI/ML Engineer role at Ionis Pharmaceuticals?
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About This Role
Headquartered in Carlsbad, California, and with offices in Boston, Massachusetts, and Dublin, Ireland, Ionis has been at work for more than three decades discovering medical breakthroughs that have redefined life for people with serious diseases. We’re pioneers in RNA\-targeted medicines, and our platform continues to revolutionize drug discovery and transform lives for patients with unmet needs. With multiple marketed medicines and a leading pipeline in neurology, cardiology and select areas of high patient needs, we continue to drive innovation in RNA therapies in addition to advancing new approaches in gene editing to provide greater value to patients and are well positioned financially to deliver on our strategic goals.
At Ionis, we pride ourselves on cultivating a challenging, motivating and rewarding environment that fosters innovation and scientific excellence. We know that our success is a direct result of the exceptional talents and dedication of our employees.
With an unprecedented opportunity to change the course of human health, we look to add diverse individuals, skill sets and perspectives to our exceptional team. We continue to invest time, money and energy into making our onsite, hybrid and remote work environments a place where solid and lasting relationships are built and where our culture and employees can thrive.
We’re building on our rich history, and we believe our greatest achievements are ahead of us. If you’re passionate about the opportunity to have meaningful impact on patients in need, we invite you to apply and join us. Experience and contribute to our unique culture while you develop and expand your career!
Ioniverse (Data \& AI) Platform Owner / Enterprise Solutions Architect
SUMMARY:
Reporting to the VP, Data \& Analytics, the Ioniverse (Data \& AI) Platform Owner / Enterprise Solutions Architect will serve as the senior technical leader responsible for operationalizing the Ioniverse as an enterprise\-scale data and AI platform centered on Databricks and modern cloud engineering practices.
This role owns the platform roadmap, technical direction, architecture standards, and delivery guardrails for the Ioniverse. It establishes reusable engineering patterns and governance controls, enables teams across enterprise domains, and helps move trusted data and AI solutions into production safely, quickly, and consistently.
Location: *preference is onsite/hybrid but willing to consider remote*
What Success Looks Like:
- Ionis has a clear, enforced, modular, and future\-ready architecture for data and AI solutions on the Ioniverse.
- Teams move data and AI solutions from sandbox to production faster with less reinvention, stronger controls, and more consistent delivery practices.
- Trusted data, semantic assets, and solution patterns are discoverable, governed, and reusable, powering AI\-driven insights, automation, and business workflows across the enterprise.
- Governance and platform standards support both speed and compliance while reducing fragmentation and strengthening internal capability across enterprise domains.
RESPONSIBILITIES:
- Own the Ioniverse platform roadmap, technical direction, and architecture standards for enterprise data and AI solutions, with Databricks as the core platform foundation.
- Define and implement target\-state patterns for data pipelines, semantic access, AI enablement, observability, security, and production support.
- Lead the design, automation, optimization, and operational support of data engineering environments across development, test, and production.
- Establish onboarding standards, guardrails, and promotion paths that move domain data products from sandbox experimentation to certified, trusted production use.
- Define and enforce standards for data quality, metadata, lineage, certification, stewardship, access control, and reuse in partnership with governance, privacy, compliance, and security stakeholders.
- Build reusable accelerators, templates, reference architectures, playbooks, and shared platform services that improve delivery speed, consistency, scalability and maintainability across teams.
- Implement DevOps best practices for CI/CD, monitoring, cost optimization, and MLOps to support advanced analytics and AI initiatives in production.
- Partner with BRPs, portfolio leads, domain product owners, data stewards, and technical teams to align priorities, intake, definitions, service levels, and certification expectations.
- Assess tools, vendors, and partner recommendations with a bias toward leveraging Ionis investments, reducing fragmentation, and strengthening long\-term internal capability.
- Partner across data engineering, data science, AI engineering, data intelligence, and business teams to accelerate delivery of priority data and AI solutions into trusted production use.
- Define interoperable data, workflow, and semantic patterns that enable consistent adoption across Commercial, Research, Development, Finance, and other enterprise domains.
- Experience leading consulting or partner teams and delivering business value in Agile, iterative environments.
- Ensure solution patterns incorporate appropriate security, role\-based access, lifecycle controls, fit\-for\-purpose AI and analytics delivery patterns, and sound cloud cost management.
- Lead consulting and vendor partners while serving as a hands\-on platform leader who prototypes, troubleshoots, and helps implement the architecture and patterns they define.
REQUIREMENTS:
- Significant experience in data platform, data engineering, analytics engineering, AI/ML platform, or architecture roles with hands\-on delivery responsibility.
- Strong experience with Databricks and cloud\-based data platforms in enterprise production environments.
- Proven ability to design and implement production\-grade data, analytics, automation, and AI solutions.
- Experience with cloud architecture, pipeline design, data engineering, DevOps, and production support.
- Experience defining standards for data quality, metadata, lineage, governance, access control, and lifecycle management.
- Strong SQL and Python skills, with the ability to work directly with engineers and technical platform teams.
- Ability to translate business priorities into practical architecture, roadmaps, engineering standards, and execution decisions.
- Strong judgment in architecture trade\-offs, build\-versus\-buy decisions, and vendor evaluation.
- Experience in life sciences or another regulated industry, including enterprise data governance, cross\-functional data product design, and platform operating models.
- Familiarity with Unity Catalog, MLflow, semantic layers, AI\-assisted analytics, and enterprise systems spanning SAP, CRM, ERP, Commercial, and Clinical domains.
- 15 years of related experience with a bachelor’s degree or 12 years and a master’s degree.
Please visit our website, http://www.ionis.com (http://www.ionis.com) for more information about Ionis and to apply for this position; reference requisition \#IONIS004049
Ionis offers an excellent benefits package! Follow this link for more details: Ionis Benefits (https://ionis.com/careers\#:\~:text\=Highly%20competitive%20benefits)
Full Benefits Link: https://ionis.com/careers\#:\~:text\=Highly%20competitive%20benefits (https://ionis.com/careers\#:\~:text\=Highly%20competitive%20benefits)
The pay scale for this position is $159,053\-$268,000
NO PHONE CALLS PLEASE. PRINCIPALS ONLY.
*Ionis Pharmaceuticals, Inc. and all its subsidiaries are proud to be EEO employers.*
Equal Opportunity Employer
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
Salary Context
This $159K-$268K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Ionis Pharmaceuticals, 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 $185,000 based on 13,200 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($213K) sits 15% above the category median. Disclosed range: $159K to $268K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Ionis Pharmaceuticals AI Hiring
Ionis Pharmaceuticals has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Carlsbad, CA, US. Compensation range: $268K - $268K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 median, while Prompt Engineer roles sit at $140,000. 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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