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About This Role
About RockWallet
Rock Solid. Rock Confident.
RockWallet is a financial technology company made up of people who think differently about how digital assets can be managed, accessed, and used.
At RockWallet, our vision is for anyone to be able to access and thrive in the digital economy. It’s our mission to help our customers make the most of these opportunities by building products that empower people to navigate digital asset usage easily, securely, and with confidence. Our self\-custodial, multicurrency wallet puts you in charge of your digital assets. RockWallet’s app makes it quick and easy to buy, use, store, and swap top cryptocurrencies, all in one place, on your mobile phone. We are a customer\-focused company obsessed with providing the best customer experience and customer support. RockWallet is registered with FinCEN as a Money Service Business. Find out more here at www.rockwallet.com.
Want to join us? We’re expanding our team globally, looking for the right people to help us grow.
Role Overview
We’re looking for a Data Science \& Machine Learning Intern to join the RW Data Team and support the development of analytics and predictive models that drive real business decisions. This role offers hands\-on exposure to applied data science in a production environment, working with real customer, product, and operational data.
You will contribute to projects such as customer behavior modeling, anomaly detection, and forecasting key metrics, while learning how data science solutions are built, validated, and deployed in practice.
This internship bridges theory and application, combining statistical thinking, experimentation, and practical machine learning to generate actionable insights.
Key Responsibilities
- Assist in developing and evaluating predictive models to understand customer behavior (e.g., engagement, churn, conversion).
- Support anomaly detection analyses to identify unusual patterns in product, marketing, or financial data.
- Help build forecasting models for key metrics such as transaction volume, revenue, and customer activity.
- Work with senior data scientists and data engineers to prepare data, engineer features, and test models.
- Analyze large datasets from multiple sources to identify trends and opportunities for optimization.
- Contribute to dashboards, reports, and internal tools that surface insights to stakeholders.
- Collaborate with product, marketing, and operations teams to understand business questions and define success metrics.
- Document analyses, assumptions, and results clearly for both technical and non\-technical audiences.
- Explore and experiment with new data science techniques, tools, and models under guidance.
Qualifications \& Requirements
- Currently pursuing or recently completed a degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.
- Strong foundation in Python (pandas, numpy, scikit\-learn; PySpark is a plus).
- Working knowledge of SQL for querying and aggregating data.
- Understanding of basic statistical concepts, regression, classification, and model evaluation.
- Familiarity with time\-series data, forecasting, or anomaly detection concepts (academic or project\-based).
- Comfortable working with messy, real\-world datasets and learning data cleaning techniques.
- Strong analytical thinking and curiosity about how data translates into business impact.
- Good communication skills and willingness to ask questions and learn.
Nice to Have
- Coursework or projects involving machine learning, forecasting, or anomaly detection.
- Exposure to AWS or cloud data tools (S3, Redshift, Glue, SageMaker) through school or projects.
- Experience with data visualization tools (QuickSight, Power BI, Tableau, or similar).
- Interest in fintech, transactional data, fraud analytics, or customer segmentation.
- Familiarity with notebooks, Git, or basic ML pipelines.
- Exposure to NLP or LLM\-based analytics (coursework or side projects).
What You’ll Gain
- Hands\-on experience working with real production\-scale data.
- Exposure to end\-to\-end data science workflows — from raw data to insights and models.
- Opportunity to contribute to projects that directly impact product and business decisions.
- A strong foundation for future roles in Data Science, Machine Learning, or Analytics.
HOW TO APPLY: Please submit your resume in our preferred file – .PDF not in .DOC. Thank you.
We thank all interested applicants; however, only those under consideration will be contacted.
RockWallet, LLC is an Equal Employment Opportunity/ Veterans/Disabled/LGBT and Affirmative Action employer. We are committed to diversity and building a team that represents a variety of backgrounds, perspectives, and skills. We do not discriminate and all decisions we make are made on the basis of qualifications, merit, and business need. Our goal is to be one global diverse team that is representative of our customers, in an inclusive environment where we can continue to innovate and grow together.
This job posting may involve the use of artificial intelligence (AI) — such as automated resume screening or candidate assessment — at one or more stages of the recruitment process. If AI tools are used, all decisions are overseen by a human reviewer.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At RockWallet, 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 $214,900 based on 6,420 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $110,000.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
RockWallet AI Hiring
RockWallet has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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