Interested in this AI/ML Engineer role at Infoblox?
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About This Role
At Infoblox, every breakthrough begins with a bold “what if.”
What if your ideas could ignite global innovation?
What if your curiosity could redefine the future?
We invite you to step into the next exciting chapter of your career journey. Bring your creativity, drive, your daring spirit, and feel what it’s like to thrive on a team big enough to make an impact, yet small enough to make a difference. Our cloud-first networking and security solutions already protect 70% of the Fortune 500 , and we’re looking for creative thinkers ready to push that influence even further. Join us and discover how far your bold “what if” can take the world, your community, and your career.
Here, how we empower our people is extraordinary: Glassdoor Best Places to Work 2025, Great Place to Work-Certified in five countries, and Cigna Healthy Workforce honors three years running — and what we build is world-class: recognized as CybersecAsia’s Best in Critical Infrastructure 2024 — evidence that when first-class technology meets empowered talent, remarkable careers take shape. So, what if the next big idea, and the next great career story, comes from you? Become the force that turns every “what if” into “what’s next”.
In a world where you can be anything, Be Infoblox.
Enterprise AI Portfolio Manager
We have an opportunity for an Enterprise AI Portfolio Manager to join our IT organization in Tacoma, reporting to the Senior Director, Enterprise AI. In this pivotal role, you will own the Enterprise AI portfolio and help build what is in it. You will give leadership a clear, trusted view of what is being delivered, when and with what result. You will also go deep on the technology: understanding what each request really needs, designing the approach and building AI solutions yourself. Collaborating closely with developers, business stakeholders, leadership, Enterprise Architecture, Security and Data teams, you will work with global teams to turn AI ambition into a sequenced, well-run portfolio of working solutions that deliver measurable business value.
Be a Contributor — What you’ll do:
- Own the Enterprise AI portfolio end to end, keeping a single source of truth for every initiative's scope, priority, status, dates, dependencies, risks and owners
- Design and build AI agents, assistants and automated workflows hands-on using Anthropic Claude, OpenAI, Google Gemini, Glean and AWS Bedrock, from prototype to production
- Use AI coding tools such as Claude Code, OpenAI Codex, Cursor and GitHub Copilot to build and ship faster
- Assess the AI approach behind each request, choosing the right model, platform and agent pattern, and judging feasibility, cost, effort and risk before work is committed
- Evaluate new models, agent frameworks and AI tools as they emerge, and recommend what to adopt
- Run portfolio planning and prioritization with leadership and business stakeholders, balancing business value, effort, risk and available capacity
- Plan capacity across internal developers and contractor teams, and adjust as demand changes
- Build and maintain the Enterprise AI roadmap, translating company priorities into a sequenced, realistic delivery plan
- Manage cross-team dependencies and risks with Enterprise Architecture, Security, Data, Infrastructure and business teams
- Define and report portfolio metrics, including throughput, cycle time, on-time delivery and capacity utilization
- Measure business value after launch, reporting results against the success criteria agreed at the start
- Prepare and lead Quarterly Business Reviews (QBRs), covering portfolio performance, value delivered, roadmap progress, capacity and upcoming priorities
- Deliver executive readouts to IT and company leadership, turning portfolio data into a clear story, decisions needed and recommendations
- Provide regular status to executive sponsors, raising risks early with options and recommendations
- Improve how the team plans, builds and ships AI work, and establish reusable patterns, standards and best practices
Be Prepared — What You Bring:
- 12+ years in technology delivery, including hands-on building and 5+ years leading portfolio or program management across multiple teams
- 2+ years hands-on building generative and agentic AI solutions with frontier models such as Anthropic Claude, OpenAI GPT, Glean and Google Gemini
- Hands-on experience with AI cloud platforms such as AWS Bedrock, Azure AI Foundry and Google Vertex AI
- Experience with agent frameworks and protocols such as Claude Agent SDK, OpenAI Agents SDK, LangGraph, Model Context Protocol (MCP) and Agent2Agent (A2A)
- Strong grasp of prompt and context engineering, retrieval-augmented generation (RAG), vector databases, tool use and multi-agent orchestration
- Experience with AI evaluation, observability and guardrails, including tracking model quality, cost and token usage
- Daily user of AI coding tools such as Claude Code, Cursor, OpenAI Codex or GitHub Copilot
- Proven ability to plan capacity and make trade-offs between demand and capacity across internal and contractor teams
- Strong command of portfolio tooling and reporting, such as Jira, with a track record of building dashboards leaders actually use
- Experience defining and reporting delivery metrics and business value, not just activity
- Proven ability to influence senior leaders and make clear prioritization recommendations when priorities compete
- Proven experience preparing QBRs and executive readouts, with strong storytelling, presentation and writing skills for executive and C-level audiences
- PMP, PgMP, SAFe or equivalent certification preferred
- Bachelor's degree in Computer Science, Engineering, Information Technology or a related field, or equivalent practical experience
Be Successful — Your Path
First 90 Days: Immerse in our culture, connect with mentors, and map the systems and stakeholders that rely on your work.
Six Months: Run portfolio planning and prioritization end to end, lead the Enterprise AI QBR, report portfolio metrics to leadership on a regular cadence, and deliver at least one AI solution you have built yourself.
One Year: Deliver a predictable, high-value portfolio with improving delivery metrics, measured business results for top initiatives, and reusable build patterns adopted across the team.
Belong— Your Community
Our culture thrives on inclusion, rewarding the bold ideas, curiosity, and creativity that move us forward. In a community where every voice counts, continuous learning is the norm. So, whether you code, create, sell, or care for customers, you’ll grow and belong here.
Be Rewarded — Benefits That Help You Grow, Thrive, Belong
- Comprehensive health coverage, generous PTO, and flexible work options
- Learning opportunities, career-mobility programs, and leadership workshops
- Sixteen paid volunteer hours each year, global employee resource groups, and a “No Jerks” policy that keeps collaboration healthy
- Modern offices with EV charging, healthy snacks (and the occasional cupcake), plus hackathons, game nights, and culture celebrations
- Charitable Giving Program supported by Company Match
- We practice pay transparency and reward performance. Offers reflect role location, internal equity, experience, skills, education, and certifications. Base salary for this position: $122,000 - $183,920 plus corporate bonus
Ready to Be the Difference?
Infoblox is an Affirmative Action and Equal Opportunity Employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, gender, sexual orientation, national origin, genetic information, age, disability, veteran status, or any other legally protected basis
#LI-MP1
#LI-Hybrid
Salary Context
This $122K-$183K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Infoblox, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($152K) sits 28% below the category median. Disclosed range: $122K to $183K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Infoblox AI Hiring
Infoblox has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Tacoma, WA, US. Compensation range: $183K - $183K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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