Interested in this AI/ML Engineer role at Fireblocks?
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About This Role
The world of digital assets is accelerating in speed, magnitude, and complexity, opening the door to new ways for leveraging the blockchain. Fireblocks' platform and network provide the simplest and most secure way for companies to work with digital assets and it trusted by some of the largest financial institutions, banks, globally\-recognized brands, and Web3 companies in the world, including BNY Mellon, BNP Paribas, ANZ Bank, Revolut, and thousands more.
#### About the Team
Join our global Data team, which partners closely with Product, Engineering, and GTM stakeholders to power data\-driven decision making and AI\-driven products across Fireblocks. The team has evolved from providing deep analysis to becoming enablers, bringing the right data and AI tools so that Sales, Marketing, and Customer Operations can run analysis and forecasting. We operate in a fast\-paced, high\-growth environment where much of what we build is new, and we figure things out as we go.
#### What You'll Do
- Design and build LLM\-powered systems and data agents that directly improve internal capabilities, enabling GTM, Product, and Engineering teams to make faster, smarter, data\-driven decisions at scale.
- Analyze large\-scale datasets to surface strategic insights across the customer lifecycle and revenue funnel; define and track critical KPIs; and translate findings into actionable product and business recommendations.
- Evaluate and quantify the business impact of AI initiatives through rigorous experimentation, benchmarking, and model evaluation to continuously optimize LLM performance.
- Continuously monitor, refine, and evolve AI models and solutions, iterating on prompt engineering and deployment practices to keep pace with rapidly advancing capabilities.
- Ship and maintain production\-grade analytics and AI applications through modern CI/CD pipelines (GitLab/GitHub), ensuring reliable, repeatable, and observable deployments.
- Manage and influence senior business stakeholders (including CRO\-level), owning projects end\-to\-end and translating complex data and AI concepts into business outcomes.
- Leverage AI\-assisted development tools (e.g., Cursor, Claude Code) to accelerate delivery speed across the team.
#### What You'll Bring
Required
- 5\+ years of experience in AI/ML engineering or a combined data analytics and AI role.
- Strong SQL proficiency; solid Python experience is a strong plus.
- Hands\-on experience with LLMs, prompt engineering, fine\-tuning, and model evaluation pipelines.
- Strong analytical background: experience defining KPIs and communicating data\-driven recommendations to senior business stakeholders, with commercial acumen across Sales, Marketing, and revenue/pipeline.
- Experience with CI/CD pipelines using GitLab or GitHub for analytics or AI workloads.
- Demonstrated ability to own projects independently: scoping problems, navigating ambiguity, and managing stakeholder relationships without heavy oversight.
- Strong cross\-functional communication skills: able to present AI concepts and outcomes to both technical and non\-technical audiences, up to CRO level.
- Ability to work in our NYC office \~3 days in office weekly
Nice to Have
- Practical knowledge of Snowflake as a database agent platform, including MCP, database agents, and semantic views/YAMLs, highly valued given our current stack.
- Experience with dbt (dbt Cloud preferred) for analytics engineering workflows.
- Familiarity with the digital assets, fintech, or Web3 domain.
#### What Success Looks Like
Within your first year, you have delivered AI solutions and data agents with clear business impact across GTM and Product, become a trusted partner for data\-driven AI initiatives across the org, and helped elevate the team's velocity and capabilities.
*For employees hired to work from our NYC HQ, Fireblocks is required by law to include a reasonable estimate of the compensation range for this role. This range is specific to New York City and takes into consideration a wide range of factors that are reviewed when making a hiring decision, such as years of experience, skills, and other business needs. I**t is not typical for a candidate to be hired at or near the top of the pay range and each compensation decision is dependent on each individual case. A reasonable base salary range estimate for this position is $156,000 \- $204,000\. The base salary is one component of the total compensation package, which for some roles may include a target bonus, a very competitive equity grant, and very generous benefits.*
*While we believe competitive compensation is a critical aspect of your decision to join us, we do hope you also spend time considering why our mission and culture are right for you. We are creating something transformational here, and we hope you are as excited about the future as we are.*
*Fireblocks' mission is to enable every business to easily and securely access digital assets and cryptocurrencies. In order to do that, we strongly believe our workforce should be as diverse as our clients, and this is why we embrace diversity and inclusion in all its forms.*
*Fireblocks' mission is to enable every business to easily and securely access digital assets and cryptocurrencies. In order to do that, we strongly believe our workforce should be as diverse as our clients, and this is why we embrace diversity and inclusion in all its forms.*
###### *Please see our candidate privacy policy here**.*
Salary Context
This $156K-$204K range is below the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Fireblocks, 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. Disclosed range: $156K to $204K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Fireblocks AI Hiring
Fireblocks has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $204K - $204K.
Location Context
AI roles in New York pay a median of $210,000 across 2,448 tracked positions. That's 5% 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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