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About This Role
About CompanySage
CompanySage is the AI\-native platform for business formation and compliance. Nearly 100,000 companies run on our network, and every month we connect thousands of them to strategic partners across banking, insurance, and lending. Alongside that, our Advantage Partner (AP) Program brings CPAs, tax advisors, bookkeepers, law firms, financial advisors, and other professional partners into a referral channel built on professional trust.
We use AI throughout the platform, always with expert human oversight, and our internal teams work the same way. We move fast, and it's a genuinely fun place to do the best work of your career.
The Role
You'll own product marketing and growth for our partner business — a player\-coach role where you set the strategy and execute it. You'll report directly to the CEO, coordinate a marketing coordinator and our digital agency, and work daily with Product, Product Ops, and our sales team. You'll have a dedicated budget and real ownership from day one. You'll also inherit native AI tooling — live partner performance dashboards, AI\-generated competitive battlecards, automated reporting — and you'll be expected to enhance it and build your own.
We're hiring you for judgment and taste — the strategic calls on positioning, offer sequencing, and partner prioritization that determine whether this business compounds. Execution and operations are where agentic automation should carry the load: reporting, first\-draft content, competitive monitoring, and campaign mechanics should run through AI tooling and agents you direct, not manual hours. The less time you spend on production, the more you're doing the job right.
The partner business runs in both directions, and you'll own both:
- Strategic partner integrations — our highest\-volume motion, driving thousands of partner referrals a month. Banking (Relay, Lili), insurance (NEXT), and lending (Lendio) offers presented to the companies in our network, with more planned as we widen into adjacent spaces: payroll, tax and bookkeeping, business credit, and workspace
- The AP Program — an earlier\-stage channel with big upside: recruiting, activating, and growing accounting and financial professionals who refer formations
What You'll Own
Integration partner marketing
- Customer\-facing positioning for each integration — the right offer, at the right point in the customer journey
- Attach rate growth — optimizing how and when partner offers reach our \~100K\-company network, in coordination with Product and Ops on launches and program changes
- Co\-marketing programs with integration partners: joint campaigns, content, and promotional offers
- Partner performance reporting: attach rates, revenue contribution, and partner satisfaction
AP channel growth
- Demand gen strategy and execution for partner acquisition — paid and organic campaigns, nurture sequences, and outreach, from awareness to signed partner
- The webinar and conference program end to end: topics, speakers, production, promotion, and follow\-up, including co\-hosted content with partners like CPA.com, Accounting Today, CPA Academy, and CalCPA
- Joint go\-to\-market plans with associations, media, and ecosystem partners
Enablement \& market intelligence
- Partner\-facing and sales\-facing collateral: decks, one\-pagers, email templates, objection handling, battlecards
- A partner resource library that AEs and strategic account managers actually use
- Competitive tracking across formation platforms, registered agent services, and channel programs to sharpen our positioning
What We're Looking For
- 5\+ years in B2B or product marketing with real integration or embedded\-partner experience — you've positioned a portfolio of offers inside a product and driven adoption and attach rates, not just brand awareness
- Real, hands\-on AI adoption in your daily work: AI\-assisted content pipelines, agent\-driven research, automated competitive monitoring, or tools your team actually depends on. Come ready to show us something you've built
- A hands\-on content creator — you write, you don't just direct others to write
- Comfortable directing an agency and holding external partners accountable without a big internal team behind you
- Data\-driven and evidence\-first: you set targets, measure, and iterate
Bonus points: experience running webinar and event programs end to end; familiarity with professional channel ecosystems — accounting and tax, law firms, financial advisory, or similar advisor networks; experience marketing financial products to small businesses; HubSpot/Salesforce fluency; experience scaling a partner program from early stage.
How We'll Measure Success
Your north star is partner\-sourced revenue growth — led by integration attach rates, with AP channel growth building behind it. Supporting metrics:
- Attach rate and revenue contribution across integration partners
- Signed AP partners attributable to marketing, and activation rate (partners referring within 90 days)
- Webinar and event conversion to pipeline
- Partner retention and satisfaction
Pay: $110,000\.00 \- $130,000\.00 per year
Benefits:
- Dental insurance
- Health insurance
- Paid parental leave
- Unlimited paid time off
- Vision insurance
Work Location: In person
Salary Context
This $110K-$130K 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 Zeta Minus One, 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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($120K) sits 45% below the category median. Disclosed range: $110K to $130K.
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.
Zeta Minus One AI Hiring
Zeta Minus One has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Juan, PR, US. Compensation range: $130K - $130K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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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