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About This Role
Trusted Connectivity for AI-Powered Dominance.
Everfox delivers trusted connectivity to protect the world’s most critical environments and safeguard the sensitive data powering decision advantage.
Built for mission-critical operations, Everfox protects what matters most by securing how data moves, how users access it, and how threats are neutralized across every domain. We enable mission speed and secure collaboration across networks, domains, and allies, while ensuring the data powering AI and advanced analytics remains trusted and protected.
We don’t just defend systems; we deliver decision dominance.
- Our Purpose . Protect what matters most by enabling organizations to operate security in the most complex and high-risk digital environments.
- Our Vision. To be the most trusted authority in high-assurance cybersecurity, enabling secure collaboration across every domain.
- Our Belief. Security should never limit mission success – it should enable it.
Our Promise. We deliver absolute confidence in environments where failure is not an option.
We’re hiring for a hybrid role. You’ll work on-site with your team several days each week (typically 2–3), with the specific days set by your manager. Because regular in-person collaboration is central to this job, we look for candidates who live within about 50 miles of the office.
Everfox will consider reasonable accommodation as required by law.
Mission AI Business Unit Leader (Senior Director / Director)
Strategic Growth Organization (SGO)
Reports to: Senior Vice President, Strategic Growth Organization
Position Summary
The Mission AI Business Unit Leader is responsible for building, leading, and scaling Everfox’s Mission AI business, serving simultaneously as both the strategic business unit leader and the primary business development executive until the organization reaches sufficient scale. This role focuses on business creation, revenue generation, ecosystem development, and strategic AI partnerships across U.S. national-security markets including the Department of War, Intelligence Community, Five Eyes, NATO, and allied governments.
The leader will define strategy, recruit partners, generate pipeline, orchestrate cross-functional collaboration, and position Everfox as the secure information-exchange layer bridging mission data with frontier AI models, accelerated compute, agents, mission applications, and operational environments.
Responsibilities
Business Unit Leadership
- Develop and execute Mission AI business strategy, including annual and multi-year plans.
- Define priority markets, mission use cases, and pipeline/bookings objectives.
- Build and manage a national-security AI ecosystem spanning compute, cloud, models, data platforms, agents, and mission applications.
- Coordinate strategy with sales, engineering, product, marketing, and executive leadership.
- Provide strategic market intelligence to executive leadership.
AI Ecosystem Development
- Establish strategic relationships with frontier/foundation model providers such as OpenAI, xAI, Google DeepMind, Microsoft, Oracle, and emerging government-authorized model companies.
- Lead partnerships across NVIDIA’s accelerated-compute, AI Factory, and edge-AI ecosystems.
Work jointly with the Hyperscaler Business Unit to develop cloud-integrated AI solutions across AWS, Azure, Google Cloud, Oracle, and emerging AI clouds. *
Mission-Centric AI Strategy
- Recruit partners that support mission-data ingestion, normalization, analytics, workflows, and decision intelligence.
- Develop explicit strategies for agentic AI platforms capable of search, analysis, correlation, workflow automation, logistics support, cyber defense, and mission-system interaction.
Build an AI application ecosystem across intelligence, C2, cybersecurity, autonomy, logistics, space, coalition operations, and tactical edge environments. *
Opportunity Creation & Pipeline Growth
- Serve as the primary business development executive generating opportunities, pipeline, and strategic pursuits.
- Develop customer relationships across DoW CDAO, military services, Intelligence Community, program offices, laboratories, DISA, Five Eyes partners, NATO, and allied nations.
Drive commercial success measured through qualified pipeline, influenced bookings, recurring revenue, AI Factory deployments, and partner-sourced pipeline. *
Solution Development & GTM Execution
- Create repeatable Mission AI solutions, including DoW AI Arsenal use cases and AI Factory architectures.
- Define mission problems, data sources, security domains, models, agents, applications, human decision points, commercial models, and partner roles.
Combine all four Strategic Growth Organization business units to deliver integrated architectures spanning sensors, data, cloud, models, agents, mission applications, and operational outputs. *
Required Experience
- 10+ years of experience in business development, AI, cloud, cybersecurity, defense technology, or related fields.
- Proven experience building new businesses, markets, or complex ecosystems.
- Demonstrated personal pipeline-generation capability, including seven- and eight-figure opportunities.
- Strong understanding of modern AI architectures, frontier models, generative AI, and agentic AI.
- Deep familiarity with Department of War and Intelligence Community markets.
- Ability to translate technical capabilities into mission outcomes and engage with both major technology companies and startups.
- Strong executive communication skills; ability to operate independently with limited initial resources.
Active security clearance preferred; TS/SCI strongly desirable.
A reasonable estimate of the on-target earnings ("OTE") for this role is between:
$317,200.00-423,100.00 USD
The actual OTE offered may vary within the range based on a candidates' unique experience, locale, and business needs. In addition to the OTE, Everfox offers a generous benefits package including flexible PTO, a 401k match, and contribution to healthcare coverages. Our Talent Acquisition team will provide specific information regarding OTE and benefits offerings.
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Don’t meet every single qualification? Studies show people are hesitant to apply if they don’t meet all requirements listed in a job posting. If there is something slightly different about your previous experience, but it otherwise aligns and you’re excited about this role, we encourage you to apply. You could be a great candidate for this or other roles on our team.
Everfox is an equal employment opportunity employer and complies with all applicable federal, state, and local laws prohibiting discrimination. Everfox does not discriminate against any employee or applicant based on race, color, religion, sex, age, national origin, disability, veteran status, marital status, medical condition, or any other category protected by applicable law. If you are a qualified individual with a disability or a disabled veteran, you may request a reasonable accommodation if you are unable or limited in your ability to use or access the Company’s career webpage as a result of your disability. You may request reasonable accommodations by sending an email to [email protected] .
Everfox is a Federal Contractor. Certain positions with Everfox require access to controlled goods and technologies subject to the International Traffic in Arms Regulations or the Export Administration Regulations. Applicants for these positions may need to be "U.S. Persons," as defined in these regulations. Generally, a "U.S. Person" is a U.S. citizen, lawful permanent resident, or an individual who has been admitted as a refugee or granted asylum.
Applicants must have the right to work in the location to which you have applied.
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Salary Context
This $317K-$423K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Everfox, 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. This role's midpoint ($370K) sits 75% above the category median. Disclosed range: $317K to $423K.
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.
Everfox AI Hiring
Everfox has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Herndon, VA, US. Compensation range: $423K - $423K.
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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