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Mercury is looking for a technically\-fluent partnerships leader to own our presence in the AI and developer ecosystem. This isn't a traditional BD role and it's not a Developer Relationships role either, it lives at the intersection of both. You'll use a technical background to build credibility with developers and AI platform teams, then translate that credibility into deep, strategic partnerships that bring Mercury's financial infrastructure into the tools and workflows where the next generation of founders actually operate.
This is a Hunter role: identifying which AI companies, developer tools, and related AI ecosystem players Mercury should be embedded in, and then going out and making it happen. The best partnerships you close will feel less like vendor deals and more like co\-building something new.
### What You'll Do:
Hunt and close new partnerships
- Proactively identify, target, and close high\-leverage partnerships across the AI and developer ecosystem: AI coding assistants, agent frameworks, MCP\-native tooling, and developer communities
- Own the full partnership lifecycle: from sourcing and outreach through negotiation, integration scoping, and activation
- Build Mercury's presence in developer communities, attending and speaking at conferences, engaging in online communities, and becoming a known, credible voice in the AI/developer ecosystem.
Build the playbook
- Define and execute Mercury's go\-to\-market strategy for the AI and developer ecosystem, this is a category Mercury is still early in, so you'll have real ownership over how we show up
- Develop joint GTM plans with key partners, including co\-marketing, co\-sell motions, and integration launch strategies
- Feed insights from the developer ecosystem back into Mercury's roadmap and partnership strategy
Navigate the AI ecosystem
- Build Mercury's presence as a financial infrastructure partner in the AI\-native landscape: MCP integrations, AI platforms, developer tools, and the growing universe of products being built on LLMs
- Position Mercury as the default financial layer for AI\-native businesses and the platforms they're built on
- Identify where the next wave of distribution is forming before competitors do and place Mercury there.
### What We're Looking For:
- You have a technical foundation. You understand how APIs work, can evaluate integration complexity, and speak credibly with engineers and CTOs, not as a builder, but as someone who deeply understands how things are built.
- You're a hunter. You don't wait for warm intros or inbound. You identify who Mercury should be talking to, find your way into the room, and drive toward a deal. You have a track record of sourcing and closing partnerships, not just managing existing ones.
- You're embedded in the developer world. You know the AI and developer ecosystem, the platforms, the communities, the influencers, the emerging tools. Developers and technical founders see you as one of them, not as a vendor.
- You think commercially. You understand what makes a partnership valuable, can structure deals, and push conversations from "interesting" to signed.
- You're a strong communicator. You can present at a developer conference, write a compelling technical post, and hold your own in a product meeting, often in the same week.
- You're entrepreneurial. You're excited to work with early\-stage founders and AI teams who are still figuring out what they're building. You add value before asking for anything.
Nice to have: experience in fintech, developer\-facing partnerships, Developer Relations, or solutions engineering at an API\-first company.
- *Mercury is a fintech company, not an FDIC\-insured bank. Banking services provided through Choice Financial Group and Column N.A., Members FDIC.*
The total rewards package at Mercury includes base salary, equity (stock options/RSUs), and benefits.
Our salary and equity ranges are highly competitive within the SaaS and fintech industry and are updated regularly using the most reliable compensation survey data for our industry. New hire offers are made based on a candidate’s experience, expertise, geographic location, and internal pay equity relative to peers.
### Our target new hire base salary ranges for this role are the following:
- US employees in New York City, Los Angeles, Seattle, or the San Francisco Bay Area: $180,900 \- $226,100
- US employees outside of the New York City, Los Angeles, Seattle, or the San Francisco Bay Area: $162,800 \- $203,500
Mercury values diversity \& belonging and is proud to be an Equal Employment Opportunity employer. All individuals seeking employment at Mercury are considered without regard to race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, gender identity, sexual orientation, or any other legally protected characteristic. We are committed to providing reasonable accommodations throughout the recruitment process for applicants with disabilities or special needs. If you need assistance, or an accommodation, please let your recruiter know once you are contacted about a role.
\#LI\-AR2
Salary Context
This $180K-$226K range is above the median 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 Mercury, 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 in Demand for This Role
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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($203K) sits 7% below the category median. Disclosed range: $180K to $226K.
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.
Mercury AI Hiring
Mercury has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $226K - $226K.
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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