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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 OfficerThe Opportunity
Bullish Group is at an inflection point. Across Bullish and CoinDesk, we are reshaping how our business teams work, and AI is central to that ambition. This is a rare opportunity to own that transformation end to end.
As Director of AI Enablement, you will define the AI strategy for our business teams and be directly accountable for its execution. You will build and lead the AI Enablement function from the ground up — starting as the first hire, then recruiting the team needed to scale what works. You will partner closely with engineering, who have their own AI tooling and infrastructure, while owning the strategy and delivery for business team enablement.
You need the technical fluency to build AI solutions yourself, and the leadership skills to scale them through others.
The Role
You will own how AI fits into the day\-to\-day operations of every business team across the group:
- Systematically finding where AI creates the most value — proactively mapping opportunities across business units, prioritising ruthlessly, and maintaining a pipeline of high\-impact use cases
- Building and shipping AI solutions — from simple copilots to workflow automation to agent\-driven orchestration, matching the right approach to the right problem
- Enabling teams to self\-serve — creating the model, tools, and training that let business teams adopt and iterate on AI independently
What You’ll Do
- Own the AI strategy for business teams across the Bullish Group and be personally accountable for its delivery
- Build, configure, and deploy AI workflows and tools directly, setting the standard for quality and speed
- Drive a structured program that moves fast — prototype, validate, ship what works, kill what doesn't
- Create a self\-enablement model that equips business teams to adopt AI tools independently
- Build and lead the AI Enablement function from zero — recruit product managers, engineers, and process specialists as the work demands
- Set clear KPIs for adoption, efficiency, and ROI. Track them rigorously. Course correct when results fall short.
- Report to the CTO, COO, and executive leadership on progress, outcomes, and strategic direction
What You’ll Bring
- 8\+ years with a product or technical background, with demonstrated experience driving operational change through technology
- A track record of personally building and shipping solutions — not only managing their delivery
- Proficiency with AI workflow automation platforms (n8n, Make, Zapier, or similar) and AI/ML tools (LangChain, OpenAI APIs, Vertex AI, Claude) — able to prototype without waiting for engineering
- A solid understanding of LLMs, generative AI, and agentic workflows, and how to apply them in a business context
- Experience working directly with business teams to identify problems, design solutions, and deliver measurable outcomes
- Strong communication skills — able to influence senior executives and energize frontline teams in equal measure
Nice to Haves
- Experience in digital assets, financial services, or media
- Background in business process redesign or operational excellence
- Experience building a function or team from scratch
*Bullish US LLC \& CoinDesk Inc. are committed to offering competitive compensation and benefits. The anticipated base salary for this position is $225,000 \- $325,000 \+ 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 $225K-$325K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At BULLISH GLOBAL, 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 $166,983 based on 13,781 positions with disclosed compensation. Director-level AI roles across all categories have a median of $244,288. This role's midpoint ($275K) sits 65% above the category median. Disclosed range: $225K to $325K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
BULLISH GLOBAL AI Hiring
BULLISH GLOBAL has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $325K - $325K.
Location Context
AI roles in New York pay a median of $200,000 across 1,670 tracked positions. That's 9% above the national 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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