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About This Role
About This Role
As Biogen's AI Security Engineer, you are a hands-on technical specialist responsible for securing the organization's rapidly expanding use of artificial intelligence, machine learning, and generative AI technologies. This role operates at the intersection of cybersecurity engineering and AI/ML systems — ensuring that AI deployments across Biogen are resilient against adversarial threats, data poisoning, model manipulation, prompt injection, and unauthorized data exposure.
The AI Security Engineer will design, implement, and operate security controls purpose-built for AI workloads — spanning model development pipelines, inference endpoints, training data protection, and AI-assisted automation platforms. This role is responsible for translating emerging AI threat frameworks (MITRE ATLAS, OWASP AI Top 10, NIST AI RMF) into actionable engineering controls that protect Biogen's AI investments without impeding innovation velocity.
This position requires a rare combination of software engineering depth, AI/ML systems knowledge, and cybersecurity expertise. The scope demands:
Deep technical proficiency across AI/ML security, cloud security, and application security — with the ability to build production-grade security tooling, not just assess or advise
Autonomous execution — independently identifying AI security gaps, designing solutions, and implementing them end-to-end without requiring constant direction
Adversarial mindset — thinking like an attacker to identify how AI systems can be subverted, manipulated, or exploited before threat actors do
Cross-domain fluency — bridging the gap between data science teams building AI and security teams protecting the enterprise, translating between both worlds.
What You’ll Do
AI Threat Detection & Response Engineering
- Design, build, and operate detection capabilities for AI-specific threats including prompt injection, model extraction, training data poisoning, and adversarial input attacks
- Develop and maintain AI security monitoring pipelines that correlate signals across LLM interactions, API gateways, and model inference endpoints
- Engineer automated response playbooks for AI security incidents — including model quarantine, token revocation, and session termination
- Continuously research emerging AI attack vectors and translate findings into detection signatures and prevention controls
Operate and tune CrowdStrike AI Runtime Defense (AIRD) and Microsoft Purview AI governance controls across the environment *
AI Platform Security Engineering
- Implement security controls across AI development and deployment platforms (AWS SageMaker, Azure OpenAI, Databricks, internal ML pipelines)
- Secure model training environments — including data access controls, compute isolation, artifact signing, and pipeline integrity verification
- Engineer guardrails for generative AI usage — content filtering, DLP integration, output validation, and session-level access controls
- Design and enforce AI model governance controls including model registry security, version control integrity, and deployment approval gates
Perform security architecture reviews of new AI services and integrations before production deployment *
AI Risk Assessment & Framework Implementation
- Conduct technical risk assessments of AI systems using MITRE ATLAS, OWASP AI Top 10, and NIST AI RMF frameworks
- Perform adversarial testing (red teaming) of AI models and AI-integrated applications to identify vulnerabilities before production deployment
- Develop and maintain AI security standards, reference architectures, and secure development guidelines for AI/ML teams
Evaluate third-party AI services and vendor AI integrations for security posture, data handling practices, and supply chain integrity *
Security Automation & AI-Augmented Defense
- Build and maintain AI-powered security tools that augment the cybersecurity team's detection and response capabilities
- Develop integrations between security platforms (CrowdStrike, Microsoft Defender, Okta, Zscaler) and AI/ML systems for intelligent threat correlation
- Engineer automated security workflows using LLMs and agentic AI for threat hunting, alert triage, and incident investigation acceleration
- Contribute to the security team's internal AI capabilities — including MCP server development, agent frameworks, and security data pipelines
Build and maintain security scoring engines and risk quantification models that drive prioritization decisions *
Required Skills
- 5+ years of combined experience in cybersecurity engineering, software engineering, and/or AI/ML systems
- Demonstrated expertise in at least three of the following domains:
- AI/ML Security: LLM security, prompt injection mitigation, model robustness testing, adversarial ML, AI supply chain integrity
- Cloud Security Engineering: AWS, Azure, or GCP security architecture and controls implementation at scale
- Application Security: Secure SDLC, API security, code review, penetration testing, threat modeling
- Security Operations: Detection engineering, SIEM/SOAR development, incident response, threat hunting
- Hands-on proficiency with Python and demonstrable experience building production security tooling and automation
- Experience with AI/ML frameworks (PyTorch, TensorFlow, Hugging Face, LangChain) and AI cloud services (SageMaker, Azure OpenAI, Bedrock, Databricks)
- Working knowledge of AI security frameworks: MITRE ATLAS, OWASP AI Top 10, NIST AI RMF
- Experience with enterprise security platforms (CrowdStrike, Microsoft Defender, Okta, Zscaler, CyberArk, or equivalent)
- Strong understanding of LLM architectures, retrieval-augmented generation (RAG), fine-tuning security implications, and agentic AI patterns
Bachelor's Degree in Computer Science, Cybersecurity, Data Science, or related technical field required *
Job Level: Management
Additional Information
The base compensation range for this role is: $140,000.00-$187,250.00
Base salary offered is determined through an analytical approach utilizing a combination of factors including, but not limited to, relevant skills & experience, job location, and internal equity.
Regular employees are eligible to receive both short term and long-term incentives, including cash bonus and equity incentive opportunities, designed to reward recent achievements and recognize your future potential based on individual, business unit and company performance.
In addition to compensation, Biogen offers a full and highly competitive range of benefits designed to support our employees’ and their families physical, financial, emotional, and social well-being ; including, but not limited to:
- Medical, Dental, Vision, & Life insurances
- Fitness & Wellness programs including a fitness reimbursement
- Short- and Long-Term Disability insurance
- A minimum of 15 days of paid vacation and an additional end-of-year shutdown time off (Dec 26-Dec 31)
- Up to 12 company paid holidays + 3 paid days off for Personal Significance
- 80 hours of sick time per calendar year
- Paid Maternity and Parental Leave benefit
- 401(k) program participation with company matched contributions
- Employee stock purchase plan
- Tuition reimbursement of up to $10,000 per calendar year
Employee Resource Groups participation *
Why Biogen?
We are a global team with a commitment to excellence, and a pioneering spirit. As a mid-sized biotechnology company, we provide the stability and resources of a well-established business while fostering an environment where individual contributions make a significant impact. Our team encompasses some of the most talented and passionate achievers who have unparalleled opportunities for learning, growth, and expanding their skills. Above all, we work together to deliver life-changing medicines, with every role playing a vital part in our mission. Caring Deeply. Achieving Excellence. Changing Lives.
At Biogen, we are committed to building on our culture of inclusion and belonging that reflects the communities where we operate and the patients we serve. We know that diverse backgrounds, cultures, and perspectives make us a stronger and more innovative company, and we are focused on building teams where every employee feels empowered and inspired. Read on to learn more about Biogen.
All qualified applicants will receive consideration for employment without regard to sex, gender identity or expression, sexual orientation, marital status, race, color, national origin, ancestry, ethnicity, religion, age, veteran status, disability, genetic information or any other basis protected by federal, state or local law. Biogen is an E-Verify Employer in the United States.
Salary Context
This $140K-$187K range is below 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 Biogen, 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. This role's midpoint ($163K) sits 23% below the category median. Disclosed range: $140K to $187K.
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.
Biogen AI Hiring
Biogen has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Triangle, NC, Remote, US. Compensation range: $187K - $187K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% of all AI roles offer remote work.
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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