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About This Role
We at Athena empower exceptional talent with boundless opportunities to achieve more. We achieve this by pairing exceptional Executive Assistants (EAs) from the Philippines and Kenya with our driven clients and ensuring both receive ongoing support throughout the journey. The result is 10x more leverage, more time, and a greater impact on our clients' work and lives.
With a waitlist growing by the day, Athena has already showcased compelling demand. As part of the Athena team, you will have an exponential opportunity, empowering the fiercely ambitious to reach the most audacious goals.
Role Overview
Athena is looking for a Revenue Operations & AI Systems Lead to run Revenue Operations for our Sales organization, reporting directly to the VP, Sales. This is a player-coach role. You'll lead a team of RevOps and Sales Ops specialists while designing and building the revenue stack hands-on: CRM, billing and payments integrations, lead routing, enrichment, referral, and checkout automation.
You'll also own how we put AI to work across the way we sell, onboard, bill, and expand. Athena's clients buy a secure, AI-augmented human-in-the-loop model, and our go-to-market should run the same way. This is a build-and-lead seat at nine-figure ARR, for someone who wants the sales team using what they ship the same week they ship it.
Duties & Responsibilities
Team Leadership
- Lead and coach a team of RevOps and Sales Ops specialists, each running a core part of the operation today.
- Set a weekly operating cadence with clear ownership, priorities, and a growth path for each person.
- Run the RevOps intake and support queue to SLA so reps get fast, consistent answers.
Revenue Systems
- Own the revenue stack end to end, including the CRM platform and data model (HubSpot), billing and payments integration, lead routing and scoring, enrichment, referral automation, and the checkout experience.
- Serve as the technical decision-maker and escalation point for systems issues across the Sales org.
- Keep data clean and reporting accurate so forecasts and pipeline reviews can be trusted.
- Write documentation the team can actually use.
AI & Automation
- Build the AI layer of the revenue motion, including lead-fit screening, agentic workflows in the sales process, automated data integrity, and AI-assisted operations.
- Take ownership of a scoped backlog of AI initiatives and decide what to keep, cut, or re-scope.
- Validate assumptions before you build, and tie what you ship to a revenue metric.
Cross-Functional Collaboration
RevOps is the connection point between Sales and the rest of Athena. On behalf of our reps and sales leadership, you'll work most closely with:
- Finance on billing, payments, forecasting, and month-end.
- Legal and InfoSec on contract terms, security reviews, and keeping deals moving through approval.
- Tech on integrations and any systems request that needs engineering support.
- Growth and Marketing on lead flow, routing, attribution, and campaign tracking.
- Operations and Client Experience on the handoff from closed deal to onboarding.
- The VP, Sales on tooling strategy, vendor relationships and renewals, and build-vs-buy decisions.
Key Stakeholders
VP, Sales, Director of Enterprise Sales, Account Executives, Finance, Legal, InfoSec, Tech, Growth, Operations
Skills
- Deep hands-on CRM administration and architecture. HubSpot strongly preferred.
- Real integration work across billing and payments, routing, enrichment, webhooks, and APIs.
- Hands-on experience building with LLMs, agents, and workflow automation, with revenue outcomes you can point to.
- Ability to explain technical decisions clearly to non-technical stakeholders.
- Strong written communication and documentation habits.
- Ability to manage priorities across several teams at once.
- Time in a quota-carrying seat, or close work alongside AEs, is a strong plus.
Competencies
- Builder: Track record of shipping systems and automation that people use every day.
- Commercial Thinker: You start with the revenue problem, then choose the tool.
- Ownership: You flag risk early, come with an ask, and don't leave surprises.
- People Leader: You coach, delegate, and grow the people around you, whether through a formal title or earned trust.
- Continuous Learner: You stay current on AI tooling and bring back what works.
Relevant Experience Required
- 5+ years across Revenue Operations, sales or marketing systems, or growth operations. We care more about what you've shipped than how many years it took.
- People leadership, formal or earned. First-time managers with a strong build record are encouraged to apply.
Experience owning a CRM and its integrations in a high-velocity B2B sales environment. *
Direct Reports and Span of Control
This role reports to the VP, Sales and leads a team of Revenue Operations and Sales Ops specialists.
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 Athena, 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. Senior-level AI roles across all categories have a median of $228,700.
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.
Athena AI Hiring
Athena has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Wilmington, NC, 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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