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About This Role
About Bullish
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Bullish is an institutionally focused global digital asset platform that provides market infrastructure and information services. These include:BullishExchange– a regulated and institutionally focused digital assets spot and derivatives exchange, integrating a high\-performance central limit order book matching engine with automated market making to provide deep and predictable liquidity. Bullish Exchange is regulated in Germany, Hong Kong, and Gibraltar.CoinDeskIndices– a collection of tradable proprietary and single\-asset benchmarks and indices that track the performance of digital assets for global institutions in the digital assets and traditional finance industries.CoinDeskData\- a broad suite of digital assets market data and analytics, providing real\-time insights into prices, trends, and market dynamics.CoinDeskInsights– a digital asset media and events provider and operator ofCoindesk.com, a digital media platform that covers news and insights about digital assets, the underlying markets, policy, and blockchain technology.
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Reports to:
Chief Technology OfficerPosition Overview
Bullish is at an inflection point. Across Bullish and CoinDesk, we are reshaping how business teams work, and AI is central to that ambition. We are building the AI Enablement function from the ground up, and we are looking for a Senior Product Manager who can help lead that build alongside the Director, AI Enablement.
This is a hands\-on, builder role. You will not be a traditional product manager who writes specs and waits for engineering. You will configure, ship, and iterate on AI workflows and tools yourself, partnering directly with business teams across Bullish Exchange, CoinDesk Data \& Indices, and CoinDesk Insights to find the highest\-leverage problems and put working AI solutions in front of users fast.
You will be the first product hired in the function and will work as a force multiplier to the Director. This is a rare opportunity to help shape both the product portfolio and the operating model of a brand new function inside a regulated global digital asset platform.
Role \& Responsibilities:
- Develop a working understanding of Bullish and CoinDesk’s business units, team structures, and operational priorities, and use that understanding to shape where the function invests.
- Conduct structured discovery across business teams to map pain points, manual workflows, and AI opportunity areas, and audit AI tools or pilots already in flight to understand what has been tried, what has worked, and what has not.
- Establish the core PM operating model for the function, including intake process, prioritization framework, backlog structure, and delivery cadence.
- Maintain a prioritized pipeline of high\-impact use cases across both Bullish and CoinDesk, prioritizing ruthlessly and making clear trade\-offs as new opportunities surface.
- Partner with the Director of AI Enablement and engineering to translate validated use cases into well\-scoped, deliverable initiatives, and write clear product specs that bridge business needs and technical implementation.
- Build and configure AI solutions hands\-on, ranging from copilots and chat assistants to workflow automations and agent\-driven orchestration, using tools such as Claude, OpenAI APIs, n8n, Make, Zapier, LangChain, and similar platforms.
- Drive delivery from requirements through launch, coordinating across engineering, business stakeholders, and the broader AI Enablement team.
- Define baseline metrics for adoption and impact, then track adoption and outcome metrics for shipped solutions and iterate based on what the data and user feedback are telling you.
- Build feedback loops with business team users to continuously improve deployed tools and workflows.
- Contribute to the self\-enablement model by creating documentation, templates, prompt libraries, and training resources that help business teams adopt and extend AI tools independently.
- Document shipped solutions clearly, covering what was built, how it works, how to maintain it, and what comes next, so each new solution makes the next one faster to ship.
- Coordinate closely with Engineering, Security, Legal, and Compliance to ensure that AI solutions meet the standards required of a regulated exchange,media and data \& indices business.
- Represent the AI Enablement function in working sessions with business unit leaders, communicating progress, trade\-offs, and outcomes in clear, non\-technical terms.
Experience \& Qualifications:
- Bachelor’s degree in a relevant field, or equivalent professional experience.
- 6\+ years of product management or comparable technical product experience, with a clear track record of personally shipping software or automation, not only managing delivery through others.
- Demonstrated proficiency with AI workflow automation platforms (such as n8n, Make, or Zapier) and direct experience building with LLM tooling (Claude, OpenAI APIs, Vertex AI, LangChain, or similar).
- Working understanding of LLMs, retrieval\-augmented generation, evaluations, and agentic workflows, and the judgment to know which approach fits which problem.
- Experience working directly with business teams to map workflows, design solutions, and deliver measurable operational outcomes.
- Strong written and verbal communication skills, with the ability to move between executive updates, hands\-on working sessions with end users, and technical conversations with engineers.
- Comfort operating in a fast\-moving, distributed organization where priorities shift and ambiguity is part of the work.
- Bias to action, strong ownership, and a problem\-solving mindset.
Bonus Points:
- Experience in digital assets, financial services, or media.
- Background in business process redesign, operational excellence, or internal tooling.
- Experience helping build a function, product line, or team from scratch.
- Familiarity with Claude Skills, MCP servers, or similar agentic patterns deployed inside a working organization.
- Comfort being the public face of a product or function with internal stakeholders, including senior executives.
*Bullish US LLC \& CoinDesk Inc. are committed to offering competitive compensation and benefits. The anticipated base salary for this position is $205,000 \- $270,000 \+ discretionary annual target bonus \+ performance incentives/benefits. Offered salary will be reflective of job related knowledge, skills and commensurate experience.*
Bullish is proud to be an equal opportunity employer. We are fast evolving and striving towards being a globally\-diverse community. With integrity at our core, our success is driven by a talented team of individuals and the different perspectives they are encouraged to bring to work every day.
Salary Context
This $205K-$270K range is above the 75th percentile for AI Product Manager roles in our dataset (median: $189K across 161 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 3,823 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At BULLISH GLOBAL, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $213,800 based on 583 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($237K) sits 11% above the category median. Disclosed range: $205K to $270K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
BULLISH GLOBAL AI Hiring
BULLISH GLOBAL has 1 open AI role right now. They're hiring across AI Product Manager. Based in New York, NY, US. Compensation range: $270K - $270K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% above the national median.
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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