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About This Role
About the Role
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We are seeking an exceptional Head of Machine Learning to lead our Fraud \& Risk Machine Learning organization. This is a highly visible leadership role responsible for building and scaling the next generation of fraud detection and risk decisioning products.
You'll lead a high\-performing ML team while remaining technically credible, partnering closely with Product, Engineering, and Executive Leadership to develop production\-grade machine learning systems that directly impact the business.
This role is ideal for a hands\-on technical leader who has successfully scaled ML products and teams in fast\-growing startup environments.
Location
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- Remote (United States)
Compensation
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- $210,000 – $250,000 base salary
- Exceptional candidates may be considered up to $260,000
- Competitive equity package
- Comprehensive benefits
- Visa sponsorship available for qualified candidates
What You'll Do
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- Lead the Fraud \& Risk Machine Learning organization, managing a team responsible for production fraud detection models.
- Define and execute the machine learning roadmap for fraud prevention, identity verification, and risk decisioning.
- Build and scale a portfolio of production ML models from concept through deployment and continuous optimization.
- Partner with Product, Engineering, Risk, and Executive Leadership to solve complex business challenges using machine learning.
- Drive end\-to\-end machine learning development including:
+ Feature engineering
+ Data preparation
+ Model development
+ Validation
+ Production deployment
+ Monitoring and model performance optimization
- Establish best practices for model governance, experimentation, and production reliability.
- Mentor and grow a high\-performing team of Data Scientists and Machine Learning Engineers.
- Provide technical leadership while remaining capable of contributing hands\-on when necessary.
- Present technical strategy, business impact, and model performance to executive stakeholders.
Required Qualifications
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- 7–15 years of experience in Applied Machine Learning or Data Science.
- 4\+ years leading and managing Machine Learning or Data Science teams.
- Proven success building and scaling production machine learning products in high\-growth startup environments.
- Experience leading teams responsible for ML systems that are core to the business.
- Strong software engineering skills with production\-level Python development.
- Deep experience across the full machine learning lifecycle:
+ Feature engineering
+ Model training
+ Model evaluation
+ Production deployment
+ Monitoring
+ Continuous improvement
- Domain expertise in one or more of the following:
+ Fraud Detection
+ Financial Risk
+ Identity Verification
+ Cybersecurity
- Experience owning multiple production ML models rather than a single isolated project.
- Strong leadership, communication, and stakeholder management skills.
- Ability to communicate technical concepts clearly to executives and cross\-functional partners.
Preferred Qualifications
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- Experience at high\-growth startups (approximately 20–400 employees).
- Track record of scaling both machine learning products and engineering organizations.
- Experience solving complex, high\-impact business problems through machine learning.
- Strong business acumen with the ability to align ML strategy to company objectives.
- Demonstrated career progression into increasingly broader technical leadership roles.
Education
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- Master's or PhD in Computer Science, Statistics, Mathematics, Physics, Engineering, or another STEM discipline preferred.
- Exceptional candidates with a Bachelor's degree and outstanding industry experience will also be considered.
Ideal Candidate
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We're looking for someone who:
- Combines deep machine learning expertise with strong software engineering fundamentals.
- Has built and deployed production ML systems at scale.
- Can balance strategic leadership with technical depth.
- Enjoys mentoring and developing high\-performing teams.
- Thrives in fast\-paced startup environments.
- Takes ownership of business outcomes—not just model accuracy.
- Is comfortable influencing technical direction and executive decision\-making.
Technical Skills
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- Python
- Machine Learning
- Feature Engineering
- Model Training \& Evaluation
- Model Deployment \& Monitoring
- Fraud Detection
- Identity Verification
- Financial Risk Modeling
- Production ML Systems
- Data Science
- Software Engineering
Salary Context
This $210K-$250K range is above the 75th percentile 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 Glint Tech Solutions, 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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($230K) sits 5% above the category median. Disclosed range: $210K to $250K.
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.
Glint Tech Solutions AI Hiring
Glint Tech Solutions has 3 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Positions span San Francisco, CA, US, Sunnyvale, CA, US. Compensation range: $250K - $270K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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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