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About This Role
Company Overview
ImmunityBio, Inc. (NASDAQ: IBRX) is a commercial\-stage biotechnology company developing cell and immunotherapy products that are designed to help strengthen each patient’s natural immune system, potentially enabling it to outsmart the disease and eliminate cancerous or infected cells. We envision a day when we no longer fear cancer, but can conquer it, thanks to the biological wonder that is the human immune system. Our scientists are working to develop novel therapies that harness that inherent power by amplifying both branches of the immune system, attacking cancerous or infected cells today while building immunological memory for tomorrow. The goal: to reprogram the patient’s immune system and treat the host rather than just the disease.
Why ImmunityBio?
- ImmunityBio is developing cutting\-edge technology with the goal to transform the lives of patients with cancer and develop next\-generation therapies and vaccines that complement, harness and amplify the immune system to defeat cancers and infectious diseases.
- Opportunity to join a publicly traded biopharmaceutical company with headquarters in Southern California.
- Work with a collaborative team with the ability to work across different areas of the company.
- Ability to join a growing company with professional development opportunities.
Position Summary
The Senior AI Cybersecurity Engineer is responsible for securing AI/ML systems end‑to‑end: from data pipelines and model training to deployment, monitoring, and abuse prevention. This role combines deep security engineering expertise with practical experience building or protecting machine learning and generative AI workloads.
You will partner with data science, platform, product, and security teams to design secure architectures for AI services, conduct threat modeling for AI/ML use cases, detect and respond to AI‑driven and AI‑targeting attacks, and help define secure development and governance practices for AI across the organization.
Essential Functions
- Design and implement security controls for AI/ML platforms, including model training environments, inference services, data pipelines, and feature stores.
- Conduct threat modeling for AI systems, including model theft, data poisoning, prompt injection, model inversion, and abuse/misuse scenarios.
- Build and maintain security tooling and automation to detect and prevent AI specific attacks (e.g., adversarial inputs, prompt injection chains, anomalous usage patterns).
- Collaborate with data scientists and ML engineers to integrate security into the AI development lifecycle (secure coding, model validation, testing and red teaming for AI behaviors).
- Evaluate and harden integrations with third party AI providers (LLM APIs, vector databases, orchestration frameworks, agents), including authentication, authorization, data handling, and logging.
- Collaborate with cloud/platform teams to ensure AI infrastructure (Kubernetes, GPU clusters, model registries, CI/CD) follows security best practices and compliance requirements.
- Define and implement monitoring for AI systems, including abuse detection, drift and anomaly alerts, model access patterns, and security relevant telemetry.
- Partner with incident response teams on investigations involving AI systems, including analyzing logs, traffic, model behavior, and potential data/model compromise.
- Contribute to policies, standards, and guardrails for responsible and secure use of AI internally (e.g., data classification rules for training inputs and prompts, allowed use cases, evaluation requirements).
- Provide technical guidance and mentorship to other engineers on AI security concepts, threats, and secure design patterns.
- Stay current on emerging AI threats, vulnerabilities, frameworks, and regulatory trends, and translate them into practical recommendations for the organization.
- Create, edit and adhere to Standard Operating Procedures (SOPs), process improvements, and standardization of templates.
- Performs ad\-hoc and cross\-functional duties and/or projects as assigned to support business needs and provide developmental opportunities.
Education \& Experience
- Bachelor’s Degree with 8\+ years of relevant experience is required; OR
- High school Diploma or equivalent with at least 12 \+ years of relevant experience is required.
- 5\+ years of hands\-on security engineering experience (application, product, cloud, or infrastructure), including designing and implementing security controls in production environments is required.
- Practical experience with AI/ML systems (e.g., working with ML pipelines, LLM applications, vector search, or MLOps platforms), whether as a security engineer or as an engineer collaborating closely with ML teams is required.
- Experience implementing authentication/authorization, secrets management, network segmentation, and secure CI/CD for services and APIs is required.
- Experience securing LLM and generative AI applications (e.g., RAG architectures, AI agents, chatbots, code assistants) is preferred.
- Experience with security logging/observability stacks (SIEM, data lakes, security analytics) and building detections for AI‑related threats is preferred.
- Relevant certifications e.g., cloud security, offensive security, or AI‑focused credentials is preferred.
Knowledge, Skills, \& Abilities
- Strong understanding of modern cloud platforms (AWS, Azure, or GCP) and container/orchestration technologies (Docker, Kubernetes) as they apply to AI workloads.
- Solid knowledge of common AI/ML threat scenarios: data poisoning, model theft/exfiltration, adversarial examples, model inversion, prompt injection, jailbreaks, and abuse/misuse of generative models.
- Proficiency in at least one major programming language used in security and ML ecosystems (Python preferred; Go, Java, or similar also valuable).
- Strong background in secure software development practices, code review, and security design reviews.
- Ability to communicate complex technical risk and tradeoffs clearly to both technical and non\-technical stakeholders.
- Hands‑on work with ML stacks such as PyTorch, TensorFlow, scikit‑learn, or ML platforms like SageMaker, Vertex AI, Azure ML, or on‑prem equivalents.
- Familiarity with AI security and safety frameworks or guidance (e.g., NIST AI RMF, ISO/IEC AI standards, major cloud provider AI security patterns).
- Background in red teaming or offensive security focused on applications, APIs, or AI systems.
Working Environment / Physical Environment
- The position will work onsite.
- Regular work schedule is Monday – Friday, within standard business hours. Flexibility is available with manager approval.
- Must possess mobility to work in a standard office setting and to use standard office equipment, including a computer.
- Lift and carry materials weighing up to 20 pounds.
This position is eligible for a discretionary bonus and equity award. The annual base pay range for this position is below. The specific rate will depend on the successful candidate’s qualifications, prior experience as well as geographic location.
$148,500 (entry\-level qualifications) to $165,000 (highly experienced) annually
The application window is anticipated to close on 60 days from when it is posted or sooner if the position is filled or closed.
ImmunityBio employees are as valuable as the people we serve. We have built a resource of robust benefit offerings to best support the total wellbeing of our team members and their families. Our competitive total rewards benefits package, for eligible employees, include: Medical, Dental and Vision Plan Options • Health and Financial Wellness Programs • Employer Assistance Program (EAP) • Company Paid and Voluntary Life/AD\&D, Short\-Term and Long\-Term Disability • Healthcare and Dependent Care Flexible Spending Accounts • 401(k) Retirement Plan with Company Match • 529 Education Savings Program • Voluntary Legal Services, Identity Theft Protection, Pet Insurance and Employee Discounts, Rewards and Perks • Paid Time Off (PTO) includes: 11 Holidays • Exempt Employees are eligible for Unlimited PTO • Non\-Exempt Employees are eligible for 10 Vacation Days, 56 Hours of Health Pay, 2 Personal Days and 1 Cultural Day • We are committed to providing you with the tools and resources you need to optimize your Health and Wellness.
At ImmunityBio, we are an equal opportunity employer dedicated to diversity in the workplace. Our policy is to provide equal employment opportunities to all qualified persons without regard to race, gender, color, disability, national origin, age, religion, union affiliation, sexual orientation, veteran status, citizenship, gender identity and/or expression, or other status protected by law.
Salary Context
This $148K-$165K range is below the median 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 ImmunityBio, 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 ($156K) sits 12% below the category median. Disclosed range: $148K to $165K.
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.
ImmunityBio AI Hiring
ImmunityBio has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in El Segundo, CA, US. Compensation range: $165K - $165K.
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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