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About This Role
AI Security Sales Regional Sales Director A10 Networks is seeking a high-performing, customer-focused AI & Security Sales Specialist to drive net new revenue and accelerate adoption of A10's AI, Security and Application Security solutions. This is a quota carrying, field facing individual contributor role with direct responsibility for building pipeline, developing executive relationships, leading complex enterprise sales cycles, and closing business.
The AI & Security Sales Specialist will work closely in partnership with regional account teams, Sales Engineering, Channel partners, Marketing and Product.
The ideal candidate brings a strong track record of cybersecurity sales execution success combined with a solid understanding of how enterprises are adopting generative AI, large language models (LLMs), AI applications, and agents and the evolving security, governance, and application protection requirements that come with them.
What You Will Drive
- Own a regional revenue target for A10 Software, AI & Security, and Application Security solutions, with primary accountability for net-new pipeline creation, opportunity progression, and closed revenue
- Develop and execute region-specific go-to-market (GTM) strategies focused on new customer acquisition and pipeline generation for A10’s AI & Security solutions
- Proactively prospect into strategic accounts and create new opportunities with CISOs, CIOs, security leaders, application and infrastructure teams, AI/ML leaders, data science teams, and other technical and business stakeholders
- Build and manage a healthy pipeline of qualified opportunities with sufficient coverage to consistently achieve revenue targets
- Lead complex, consultative enterprise sales cycles from initial engagement through negotiation and close, coordinating internal and external stakeholders to drive customer decisions
- Develop compelling business cases that connect AI and application-security risks to measurable customer outcomes, including risk reduction, secure AI adoption, governance, operational resilience, and protection of business-critical applications
- Collaborate with Sales Engineering, Product, Marketing, Channel and other GTM teams to develop and execute customer-specific strategies and Sales Plays
- Partner closely with regional account teams to identify whitespace, develop account strategies, and execute strategic AI & Security Sales Plays which maintain clear ownership of the specialist opportunity and sales outcome
- Build trusted relationships with strategic customers and partners and industry influencers; lead consultative sales cycles from engagement to close
- Articulate the value proposition of A10 Networks’ AI & security offerings and use cases
- Evangelize A10’s AI & Security capabilities through customer briefings, industry events, executive briefings, partner engagements and thought-leadership opportunities
- Provide accurate forecasting and communicate pipeline health, competitive intelligence, customer needs, and market trends to sales and leadership teams
Build strong partnerships with the regional sales team *
What Success Looks Like
- Consistent attainment or overachievement of assigned quota
- Strong self-generated pipeline with sufficient coverage to support and exceed revenue targets
- Growth in net new logos and expansion of A10's AI & Security
- Expansion of A10’s Security footprint within strategic accounts
- Successful execution of strategic AI & Security sales plays across the region
- Strong executive-level customer relationships and trusted-advisor status within target accounts
- Strong collaboration with regional sales, sales engineering, channel, and marketing teams without losing clear ownership of the sales outcome
Accurate forecasting, disciplined pipeline management, and strong sales execution *
Qualifications / Experience
Required / Preferred:
- 8+ years of successful enterprise sales experience in cybersecurity, cloud, or AI related technologies
- Proven track record of consistently meeting or exceeding quota and generating net-new revenue
- Proven experience building pipeline through prospecting and new customer acquisition , not solely managing inbound or existing-account opportunities
- Understanding of AI technologies (generative AI tools, machine learning, large language models/LLMs).
- Strong understanding of cybersecurity fundamentals (data security, network security, cloud security, and risk management)
- Demonstrated ability to lead complex, multi-stakeholder enterprise sales engagements
- Experience with cloud platforms (AWS, Azure, or GCP)
- Experience selling to CISOs, CIOs, and data science/security teams
- Excellent executive presence, communication, presentation, negotiation and consultative selling skills; ability to influence senior stakeholders
- Ability to translate complex technical and security concepts into compelling business value for both technical and executive audiences
- Self-starter who thrives in a fast-paced, collaborative environment and comfortable operating with significant autonomy
- Experience in high-growth or scale-up environments
- Background working with/through VARs, cloud marketplaces, or CSP partnerships
Bachelor’s degree in business, computer science, engineering, or related field preferred (advanced degrees a plus) *
Company Context
A10 Networks (NYSE: ATEN) delivers secure application and network solutions that protect, optimize, and scale business-critical systems across on-premises, hybrid cloud, and edge environments. Our portfolio enables large enterprises, service providers, and cloud platforms worldwide to deliver performance, reliability, and protection against cyber threats, while preparing their networks for the demands of AI and next-generation applications. Founded in 2004 and headquartered in San Jose, California, A10 Networks serves over 7,000 global customers.
A10 Networks is an equal opportunity employer and a VEVRAA federal subcontractor. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, or any other characteristic protected by law. A10 also complies with all applicable state and local laws governing nondiscrimination in employment.
#LI-AN1 - Hybrid
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At A10 Networks, 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 $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
A10 Networks AI Hiring
A10 Networks has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Dallas, TX, US, San Jose, CA, US, Cumming, GA, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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