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About This Role
About Gen:
Gen is a global company dedicated to powering Digital Freedom through its trusted consumer brands including Norton, Avast,
LifeLock, MoneyLion and more. Our combined heritage is rooted in financial empowerment and cyber safety for the first digital
generations, and today we deliver award\-winning cybersecurity, online privacy, identity protection and financial wellness solutions
to nearly 500 million users in more than 150 countries.
Together, we share a collective passion and vision to protect consumers and help them grow, manage and secure their digital and
financial lives. We’re always looking for smart, fearless and high\-impact talent who see AI as a teammate – leveraging it to move
faster and deliver meaningful results.
When you’re part of Gen, you’ll have the flexibility, tools and support to do your best work and grow your career – from flexible
working options and time off to competitive pay, benefits and well\-being programs.
At Gen, we are scrappy and relentlessly customer driven. We create room for healthy debate, experimentation and continuous
learning, and we seek out people with different experiences, identities and ideas to join our team. You’ll work with people who back
each other, respect each other and understand that our differences are a competitive advantage.
If this sounds like you, we’d love you to be part of Gen.
About The Role:
Our team is a core part of Gen’s AI transformation. We build machine learning systems that directly improve customer growth,
retention, personalization, pricing, recommendations, billing success, and long\-term customer value across a large global consumer
portfolio.
This role focuses on applied machine learning, experimentation, and business\-impact modeling. You will build practical models that
personalize customer decisions across in\-app messages, email, portals, billing flows, and lifecycle journeys.
We are looking for a hands\-on AI / Machine Learning Engineer who can frame business problems, build models, design experiments,
measure impact rigorously, and partner with engineering and product teams to bring models into production. Experience with
recommender systems, uplift modeling, contextual bandits, pricing, or lifecycle personalization is a strong plus.
Key Responsibilities:
- End\-to\-end ML ownership: Independently lead applied machine learning initiatives from data preparation and model development
through experimentation, production deployment, monitoring, and continuous optimization.
- Productionization and MLOps: Deploy and operate scalable ML solutions with robust workflows for batch or real\-time inference,
evaluation, monitoring, observability, versioning, retraining, rollback, and continuous model iteration.
- Experimentation and impact measurement: Design and analyze A/B tests, holdouts, and validation frameworks to measure
incremental customer and business outcomes.
- Advanced model development: Design and build propensity, response, uplift, recommendation and ranking, contextual bandit,
segmentation, optimization, and customer\-value models.
- Cross\-functional delivery: Partner with ML infrastructure, data engineering, backend engineering, product, analytics, and business
teams to integrate models into reliable production systems.
- AI\-first engineering workflows: Build agentic tools, automation, and reusable modules that streamline model development and MLOps
workflows, improve productivity, and increase the speed, quality, and consistency of ML delivery.
About You:
Education:
Degree requirements are flexible. A technical degree in Computer Science, Data Science, Statistics, Mathematics, Operations
Research, Economics, Engineering, or a related field is helpful, but equivalent practical experience is equally valued.
A Master’s or PhD in a quantitative field is a plus, but not required.
Experience:
- Applied ML experience: Five or more years of professional experience in applied machine learning, data science, ML engineering,
applied statistics, or a related field, or equivalent demonstrated impact.
- Large\-scale data: Experience building and evaluating models using large\-scale behavioral, transactional, product, marketing, or
customer data.
- Experimentation: Experience designing experiments, defining success metrics, measuring incrementality, interpreting results, and
translating findings into practical product or business decisions.
Gen \| AI / Machine Learning Engineer II
- Production collaboration and ML operations: Experience partnering with engineering, product, analytics, and business teams to deploy
and operate production ML systems, including inference pipelines, monitoring, observability, retraining, and cloud\-based MLOps
workflows.
- Relevant specialization: Experience with personalization, recommendation, ranking, uplift modeling, causal inference, contextual
bandits, pricing, optimization, or lifecycle decisioning is a strong plus.
Skills:
- Machine learning and modeling: Strong Python skills and hands\-on experience with common ML frameworks, supervised learning,
model selection, hyperparameter tuning, evaluation, and performance diagnosis.
- Data processing and feature engineering: Strong SQL skills and experience with BigQuery, Spark, or similar platforms for data
collection, cleaning, preprocessing, exploration, and feature development.
- Analytics and experimentation: Strong statistical reasoning and practical knowledge of A/B testing, holdout design, causal
measurement, incrementality, statistical significance, and business\-impact analysis.
- Production engineering and MLOps: Experience with cloud ML platforms, deployment pipelines, batch or real\-time inference, CI/CD,
model registries, monitoring, observability, retraining, rollback, and scalable system design.
Personal Attributes:
- Strong ownership: Takes responsibility for delivering high\-quality solutions and measurable outcomes with limited oversight.
- Business\-impact orientation: Connects modeling and engineering decisions to customer experience, product performance, and
business value.
- AI\-first builder mindset: Enjoys coding, modeling, automating, and shipping while proactively using AI and agentic tools to improve
productivity and quality.
- Clear, collaborative communication: Communicates assumptions, tradeoffs, risks, and results effectively across ML, engineering,
product, analytics, and business teams.
What’s Next:
Our hiring process includes the following steps:
1\. Video Introduction: Submit a brief video introducing yourself, your work, and your most relevant experience.
2\. Technical interview: Demonstrate your applied machine learning, analytical, and engineering capabilities.
3\. Hiring manager interview: Meet with the hiring manager to discuss your background and fit for the role.
4\. Final interview: Meet with our AI leadership, including the Chief AI Officer, for a final assessment.
Compensation Range: $300K
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Gen Digital Inc., 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Gen Digital Inc. AI Hiring
Gen Digital Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Mountain View, CA, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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