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About This Role
About Evertune:
Evertune is building the first AI discovery platform for modern marketers. As large language models (LLMs) become the go\-to source for recommendations – we help brands understand exactly what AI is saying about them, where they stand against competitors, and how to show up more often in AI\-powered answers. Our platform turns black\-box AI behavior into actionable insights, so marketing teams can make faster, smarter decisions that drive growth.
Founded by leaders who helped scale The Trade Desk into the world's leading ad tech platform, we bring deep expertise in digital advertising, data, and high\-growth environments. Now, we're pioneering a new category at the intersection of AI, SEO, and brand strategy.
We raised a $15M Series A led by Felicis Ventures, with support from Eniac, NextView, Roger Ehrenberg, and leaders from OpenAI, Google, Meta, Uber, and more. This gives us the momentum to move faster and build the first enterprise\-grade AI marketing suite.
Backed by top\-tier VC investors and trusted by forward\-thinking brands, we have the resources to scale – but what really defines us is our culture. We move fast, think boldly, and believe the best ideas win regardless of title. If you're excited about shaping the future of how brands grow in the age of AI, you'll thrive here.
Your Team:
You'll be joining a lean, high\-performing go\-to\-market team during a pivotal phase of Evertune's growth. Our GTM org spans sales, customer success, and marketing – working together to close business, shape strategy, and scale our client base across both brands and agencies. We operate with urgency, clarity, and trust – collaborating closely to drive impact for every partner we work with.
This role will sit at the intersection of customer success, product, and strategy. You'll work directly alongside two of our Senior AI Success Managers to support and service our largest enterprise brand and agency accounts – helping ensure clients are onboarded smoothly, educated on the platform, and set up to get the most value out of Evertune.
As one of the newer members of our CS team, you'll get hands\-on exposure to how a venture\-backed startup builds in one of the fastest\-changing spaces in tech, with direct mentorship from senior team members and close collaboration with product and engineering.
Your Role:
We're hiring an Associate AI Success Manager in our NYC office to support the servicing and success of our high\-value customers and agency partners. This is a client\-facing role focused on helping clients understand and adopt the platform – with a strong emphasis on responsiveness, clear communication, and translating insights into action.
You'll work closely with two Senior AI Success Managers on our largest accounts, helping with day\-to\-day servicing, client education, reporting, and account operations. Over time, you'll build toward owning pieces of the client relationship independently.
You'll help clients understand how their brand shows up in AI\-generated responses – walking them through insights, answering questions, and helping them see the value Evertune delivers. You should be curious, detail\-oriented, and excited to use AI tools yourself to work more efficiently and effectively.
While deep SEO or AI expertise isn't required on day one, you'll be expected to quickly ramp up on how brands show up in LLMs – and how Evertune can help them improve visibility in AI\-generated results.
You must be based in the NYC area and available to work in\-office 3x/week at our Flatiron location.
Responsibilities:
- Support two Senior AI Success Managers in servicing our largest enterprise brand and agency accounts across onboarding, adoption, and renewal
- Help support onboarding sessions and follow\-up touch points that build client confidence and platform adoption
- Educate clients on how their brand shows up in AI\-generated responses and how to interpret Evertune's platform insights
- Prepare client\-ready reporting, briefs, and recommendations based on platform data and client KPIs
- Act as a responsive point of contact for day\-to\-day client questions, escalating and coordinating with senior team members as needed
- Help track account health, renewal timelines, and expansion opportunities alongside senior CSMs
- Use AI tools as part of your own workflow to work faster and smarter – from research and data analysis to drafting client materials
- Collaborate with product and engineering to relay client feedback and help shape the roadmap
- Help document and improve CS processes and playbooks as the team scales
Requirements:
- 1–3 years in Customer Success, Account Management, or a similar client\-facing SaaS role
- Experience supporting onboarding, renewals, or account servicing for clients, ideally within a team structure
- Strong written and verbal communication skills, with comfort presenting to clients and internal stakeholders
- Genuine curiosity about AI and LLMs, and enthusiasm for using AI tools as an assistant to your own work and problem\-solving
- Highly organized, detail\-oriented, and comfortable juggling multiple accounts and workstreams
- A collaborative, coachable mindset – excited to learn from senior team members while building toward more ownership over time
- Based in NYC and available to work in\-office 3x/week
Nice\-to\-Haves
- Experience supporting accounts for holding companies or agency groups
- Background in adtech, martech, SEO tools, or content strategy
- Hands\-on familiarity with AI/LLM tools (e.g., ChatGPT, Claude, Perplexity) in a work context
Culture \& Values
At Evertune, we're building more than just groundbreaking AI products—we're building a culture where speed, insight and humanity go hand in hand. Here's what we value:
- *Curiosity:* *We start with questions, not assumptions, and follow the signal wherever it leads.*
- *Velocity:* *We move fast, iterate quickly and prefer learning through doing.*
- *Empathy:* *We're ambitious, but never at the expense of being respectful, inclusive and grounded.*
- *Openness:* *We welcome different perspectives and challenge each other's ideas.*
- *Ownership:* *We trust people to own decisions and outcomes.*
Compensation \& Benefits:
Our salary ranges are based on paying competitively for our size and industry. Salary is just one part of our total compensation package that includes equity, perks \& benefits, and development opportunities at Evertune AI. Individual pay decisions are based on several factors, including qualifications for the role, experience level, skillset, geography, and balancing internal equity relative to other Evertune AI employees. It is our expectation that the majority of candidates who are offered roles at Evertune AI will land well within our salary ranges based on these factors.
The expected annual base salary for this role is between $70,000–$90,000, plus stock options and commission\-based incentives tied to customer growth, including renewals and account expansion.
This is an exempt position.
Benefits include top\-tier Medical/Dental/Vision Coverage, HSA/FSA, Generous Paid Time Off, commuter benefits.
Evertune does not accept unsolicited resumes from search firm recruiters. Fees will not be paid in the event a candidate submitted by a recruiter without an agreement in place is hired; such resumes will be deemed the sole property of Evertune. Evertune is an equal opportunity employer. All aspects of employment will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, veteran status, or any other status protected under federal, state, or local law.
Compensation Range: $70K \- $90K
Salary Context
This $70K-$90K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At EverTune, 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 $218,750 based on 3,817 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $120,000. This role's midpoint ($80K) sits 63% below the category median. Disclosed range: $70K to $90K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
EverTune AI Hiring
EverTune has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $90K - $90K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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