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About This Role
About IonQ:
IonQ, Inc. [NYSE: IONQ] is the world's leading quantum platform and merchant supplier - delivering integrated quantum solutions across computing, networking, sensing, and security. IonQ's newest generation of quantum computers, the IonQ Tempo, is the latest in a line of cutting-edge systems that have been helping customers and partners including Amazon Web Services, and AstraZeneca achieve 20x performance results and accelerate innovation in drug discovery, materials science, financial modeling, logistics, cybersecurity, and defense. In 2025, the company achieved 99.99% two-qubit gate fidelity, setting a world record in quantum computing performance.
Headquartered in College Park, Maryland, IonQ has operations in California, Colorado, Massachusetts, Tennessee, Washington, Italy, South Korea, Sweden, Switzerland, Canada, and the United Kingdom. Our quantum computing services are available through all major cloud providers, while we also meet the needs of networking and sensing customers across land, sea, air, and space. IonQ is making quantum platforms more accessible and impactful than ever before.
Location: This role can work onsite or hybrid from one of our offices. We are open to a fully remote option for the right candidate.
Travel: Up to 10%
Job ID: 1915
The Role:
We are hiring a senior technical leader to build the AI foundation our security organization runs on — and to make sure it actually gets used. This is a dual-mandate role. You will stand up the enterprise AI infrastructure that lets security teams safely build with large language models and agents, and you will lead the adoption effort that turns that infrastructure into measurable gains across detection and response, threat intelligence, vulnerability management, GRC, and security engineering.
This is a hands-on senior individual contributor role. You will write code, design systems, and set technical direction. You will not manage a team, but you will lead through influence: partnering with engineering leaders, coaching analysts and engineers, and building the coalitions that make org-wide change stick. Expect roughly an even split between platform building and enablement work.
You will report to the security leadership team and work closely with Platform Engineering, IT, Legal, Privacy, and the broader AI/ML organization.
Responsibilities:
Stand Up the Enterprise AI Infrastructure
- Build the platform. Design and build the shared platform security teams use to develop AI applications — model gateway and routing, authentication and authorization, secrets handling, rate limiting, cost attribution, and audit logging.
- Enable agentic systems. Establish patterns and reusable components for agentic workflows: tool and MCP server integration, sandboxed execution, human-in-the-loop approval gates, and least-privilege scoping for agent credentials.
- Wire in the data. Connect security's knowledge — runbooks, detection logic, past incidents, asset inventory, policy documents — to AI systems through well-governed retrieval pipelines with correct access controls and data classification enforcement.
- Make it measurable. Build the evaluation harness, regression suites, and observability that tell us whether an AI system is working: output quality, latency, cost, hallucination rate, and drift over time. Nothing ships to production without a baseline.
- Secure the stack itself. Implement guardrails against prompt injection, data exfiltration through model outputs, insecure tool use, and supply-chain risk in models and AI dependencies. Red-team the platform and the applications built on it.
- Set the standards. Own the reference architecture, golden paths, and internal documentation so that a security engineer can go from idea to reviewed prototype in days rather than months.
Drive AI Adoption Across the Security Organization
- Find the real use cases. Work directly with detection engineering, IR, threat intel, vulnerability management, GRC, and security operations to identify where AI meaningfully reduces toil or improves quality — and where it does not. Kill weak ideas early.
- Build the flagship applications. Deliver a small number of high-visibility wins yourself. Nothing drives adoption like a working tool that saves an analyst two hours a day.
- Teach the organization. Run enablement programs — office hours, workshops, internal documentation, prompt and agent design patterns, brown-bags — that raise the AI fluency of the entire security organization, not just the engineers.
- Measure adoption honestly. Define and report the metrics that show whether adoption is real: active users, workflows automated, analyst hours returned, time-to-detect and time-to-respond improvements, quality deltas against human baselines.
- Establish governance that enables. Partner with Legal, Privacy, and Compliance to establish the acceptable-use policy, review process, and risk framework for AI in security workflows — designed to unblock teams rather than stall them.
- Stay ahead of the field. Maintain the organization's point of view on where AI capability is heading and what it means for our security roadmap, staffing, and threat model. Evaluate vendors and open models on the merits.
Requirements:
- 8+ years in security engineering, platform engineering, or a closely related technical field, including significant hands-on software development.
- Demonstrated experience building and operating production systems with large language models — not prototypes or demos. You have shipped something real, dealt with its failure modes, and iterated on it.
- Strong software engineering fundamentals and fluency in Python (or an equivalent language), including API design, distributed systems, and cloud infrastructure (AWS, GCP, or Azure).
- Deep understanding of security engineering: identity and access management, secrets management, network boundaries, logging and detection, and secure software development practices.
- Working knowledge of AI-specific risk — prompt injection, jailbreaks, data leakage through model outputs, insecure agent tool use, model and dependency supply chain — and practical mitigations for each.
- A track record of driving technical change across an organization through influence rather than authority: building consensus, teaching, and shipping things people voluntarily adopt.
- Clear written and verbal communication. You can explain a system design to an engineer and the business case for it to an executive, in the same week.
- Sound judgment about where AI is genuinely useful and where it is not. We want an advocate who is also a skeptic.
Preferred Qualifications:
- Experience with agent frameworks, orchestration, and the Model Context Protocol (MCP) or comparable tool-use standards.
- Experience building evaluation frameworks or LLM observability tooling.
- Background in security operations, detection engineering, or incident response — you have felt the toil firsthand.
- Experience with retrieval systems, embeddings, vector databases, and knowledge pipeline design.
- Familiarity with AI governance and assurance frameworks such as the NIST AI Risk Management Framework, ISO/IEC 42001, or the OWASP Top 10 for LLM Applications.
- Experience fine-tuning or evaluating open-weight models for domain-specific tasks.
- Prior experience as a first or founding hire on a platform or capability that later scaled organization-wide.
What Success Looks Like
First 90 Days
- You have mapped the current state — what security teams are already doing with AI, sanctioned or not — and published a prioritized assessment of gaps, risks, and opportunities.
- A minimum viable AI platform is running: gateway, authentication, logging, and cost attribution, with at least one security team building on it.
- An acceptable-use policy and lightweight review process for AI in security workflows is drafted and socialized.
First 6 Months
- Two or more production AI applications are in daily use by security teams, with measured impact.
- The evaluation and observability layer is in place; no AI system reaches production without a quality baseline and monitoring.
- Enablement is operating on a regular cadence, and engineers outside your immediate orbit are shipping their own AI-assisted workflows on the golden paths you built.
First Year
- AI is a normal part of how the security organization works, with adoption and impact reported through metrics leadership trusts.
- The platform supports agentic workflows safely, with guardrails that have been tested adversarially.
- The role has clearly outgrown one person, and you have made the case for what the team around it should look like.
Why This Role
Security organizations are drowning in work that AI is genuinely good at, and most of them are still stuck at the pilot stage because nobody owns both halves of the problem — the infrastructure and the adoption. This role owns both. You will have executive sponsorship, real budget, and the latitude to set technical direction from a blank page. If you want to define how an entire security organization works with AI rather than tune someone else's system, this is that job.
The total compensation package includes base, bonus, equity, and a range of benefit options found on our career site.
Compensation will vary based on individual factors such as education, qualifications, and experience of the final candidate(s), specific office location, and calibration against relevant market data and internal team equity. Posted base salary figures are subject to change as new market data becomes available. Our benefits include comprehensive medical, dental, and vision plans, matching 401(k), unlimited PTO and paid holidays, parental/adoption leave, legal insurance, and a home technology stipend. Details of participation in these benefit plans will be provided when a candidate receives an offer of employment.
At IonQ, we believe in fair treatment, access, opportunity, and advancement for all while striving to identify and eliminate barriers. We empower employees to thrive by fostering a culture of autonomy, productivity, and respect. We are dedicated to creating an environment where individuals can feel welcomed, respected, supported, and valued.
We are committed to equity and justice. We welcome different voices and viewpoints and do not discriminate on the basis of race, religion, ancestry, physical and/or mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, transgender status, age, sexual orientation, military or veteran status, or any other basis protected by law. We are proud to be an Equal Employment Opportunity employer.
US Technical Jobs. The position you are applying for will require access to technology that is subject to U.S. export control and government contract restrictions. Employment with IonQ is contingent on either verifying "U.S. Person" (e.g., U.S. citizen, U.S. national, U.S. permanent resident, or lawfully admitted into the U.S. as a refugee or granted asylum) status for export controls and government contracts work, obtaining any necessary license, and/or confirming the availability of a license exception under U.S. export controls. Please note that in the absence of confirming you are a U.S. Person for export control and government contracts work purposes, IonQ may choose not to apply for a license or decline to use a license exception (if available) for you to access export-controlled technology that may require authorization, and similarly, you may not qualify for government contracts work that requires U.S. Persons, and IonQ may decline to proceed with your application on those bases alone. Accordingly, we will have some additional questions regarding your immigration status that will be used for export control and compliance purposes, and the answers will be reviewed by compliance personnel to ensure compliance with federal law.
US Non-Technical Jobs. Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum. Accordingly, we will have some additional questions regarding your immigration status that will be used for export control and compliance purposes, and the answers will be reviewed by compliance personnel to ensure compliance with federal law.
If you are interested in being a part of our team and mission, we encourage you to apply!
Salary Context
This $180K-$225K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At IonQ, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. Disclosed range: $180K to $225K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
IonQ AI Hiring
IonQ has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in College Park, MD, US. Compensation range: $225K - $225K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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