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About This Role
About the Role
Chime's Data Science and Machine Learning team is building models, services, and platforms that transform how millions of users manage and grow their financial lives. We are looking for a hands-on Tech Lead Manager with deep technical expertise in machine learning and data science, particularly within the Growth and Marketing domain. Beyond technical proficiency, we value creativity, user empathy, and strong collaboration.
As a Tech Lead Manager within our Growth and Marketing team, you will lead a talented group of data scientists and machine learning engineers to develop innovative growth and marketing models. These models will provide critical insights into acquiring, retaining and growing Chime members, broaden access to credit, and ensure financial inclusivity. You will play a pivotal role in creating innovative, ground-up products while driving the development of cutting-edge acquisition and retention models and solutions. If you are passionate about marketing, growth, customer acquisition and retention models, this role could be a great fit for you.
In this role, you can expect to
- Lead and inspire a high-performing team of data scientists and ML engineers, ensuring the successful development and deployment of machine learning solutions for customer acquisition, conversion and retention.
- Drive strategic direction for ML initiatives in marketing, engagement, and growth by identifying opportunities where AI/ML can optimize our customer funnel.
- Oversee the end-to-end development of machine learning models such as lifetime value prediction, churn risk modeling, customer segmentation, marketing attribution, referral recommendations, and personalized communications.
- Collaborate cross-functionally with marketing, product, growth, and engineering teams to align machine learning initiatives with business objectives.
- Leverage transactional and behavioral data to enhance customer targeting, optimize acquisition spend, and improve retention strategies.
- Establish ML best practices, including model development, validation, deployment, and monitoring, ensuring scalability and business impact.
- Advocate for a data-driven culture, partnering with business leaders to drive strategic decisions through experimentation and predictive analytics.
- Stay ahead of industry trends, bringing cutting-edge AI/ML techniques into our marketing and growth strategies.
To thrive in this role, you have
- 7+ years of experience developing machine learning models for marketing and growth, from inception to production, with a focus on customer acquisition, engagement, and retention.
- 5+ years of experience leading data science teams, with a proven track record of mentoring, coaching, and driving impactful machine learning solutions.
- Strong expertise in marketing and growth analytics, including experience with customer segmentation, LTV modeling, churn prediction, referral systems, and multi-touch attribution.
- Deep understanding of AI/ML techniques, including classification, clustering, reinforcement learning, optimization, deep learning, and NLP for customer engagement.
- Hands-on experience deploying machine learning models in real-world production environments, integrating with marketing tech stacks and growth platforms.
- Strong product intuition with the ability to work iteratively in a fast-paced, cross-functional environment.
- M.S. or Ph.D. in Machine Learning, Computer Science, Statistics, or a related STEM field.
- Proficiency in Python and SQL, with experience in building ML pipelines and wrangling large-scale data.
- Experience with modern ML and data engineering technologies, such as AWS, Kafka, Airflow, Redis, MySQL, Postgres, Spark, Snowflake, Looker.
- Exceptional communication and stakeholder management skills, with the ability to partner effectively with marketing, growth, product, and engineering teams.
#LI-DA1 #LI-Onsite
A little about us
At Chime, we believe that everyone can achieve financial progress. We created Chime—a financial technology company, not a bank*—on the premise that core banking services should be helpful, easy, and free. Through our user-friendly tools and intuitive platforms, we empower our members to take control of their finances and work towards their goals. Whether it's starting a savings account, purchasing a first car or home, launching a business, or pursuing higher education, we're proud to have helped millions unlock their financial potential.
We're a team of problem solvers, dreamers, and builders with one shared obsession: our members. From day one, Chimers have worked tirelessly to out-hustle and out-execute competitors to bring our mission to life. Their grit and determination inspire us to work harder every day to deliver the very best experience possible. We each bring an owner's mindset to our work, refusing to be outdone and holding ourselves accountable to meet and exceed the highest bars for our teams, our company, and our members.
We believe in being bold, dreaming big, and taking risks, while also working together, embracing our diverse perspectives, and giving each other honest feedback. Our culture remains deeply entrepreneurial, encouraging every Chimer to see themselves as stewards of our mission to help everyday Americans unlock their financial progress.
We know that to achieve our mission, we must earn and keep people's trust—so we hold ourselves to the highest standards of integrity in everything we do. These aren't just words on a wall—our values are embedded in every aspect of our business, serving as a north star that guides us as we work to help millions achieve their financial potential.
Because if we don't—who will?
- Chime is a financial technology company, not a bank. Banking services provided by The Bancorp Bank, N.A. or Stride Bank, N.A., Members FDIC.
What we offer for our full-time, regular employees
- Our in-office work policy is designed to keep you connected - with four days a week in the office and Fridays from home for those near one of our offices, plus team and company-wide events depending on location. Whether you're coming in regularly or are part of our fully remote program, you'll stay engaged with your work and teammates.
- Benefits that support your work and life, including backup child, elder, and pet care and subsidized commuter benefits for eligible employees.
- ✨ Comprehensive health, financial, and wellbeing benefits designed to support you at every stage of life.
- Generous vacation policy and company-wide paid days off
- 1% of your time off to support local community organizations of your choice
- Annual wellness stipend to use towards eligible wellness related expenses
- Up to 22 weeks of paid parental leave for birthing parents and 12 weeks of paid parental leave for non-birthing parents
- Access to family planning reimbursement
- A challenging and fulfilling opportunity to join one of the most experienced teams in FinTech and help millions unlock financial progress
We know that great work can't be done without a diverse team and inclusive environment. That's why we specifically look for individuals of varying strengths, skills, backgrounds, and ideas to join our team. We believe this gives us a competitive advantage to better serve our members and helps us all grow as Chimers and individuals.
Chime is proud to be an Equal Opportunity Employer. We consider qualified applicants without regard to race, color, ancestry, religion, sex, national origin, sexual orientation, gender identity, age, marital or family status, disability, genetic information, veteran status, or any other legally protected basis under provincial, federal, state, and local laws, regulations, or ordinances. We will also consider qualified applicants with criminal histories in a manner consistent with the requirements of state and local laws, including the San Francisco Fair Chance Ordinance, Cook County Ordinance, NYC Fair Chance Act, and the LA City Fair Chance Ordinance, and consistent with Canadian provincial and federal laws. If you have a disability or special need that requires accommodation during any stage of the application process, please contact: [email protected].
To learn more about how Chime collects and uses your personal information during the application process, please see the Chime Applicant Privacy Notice.
Salary Context
This $199K-$275K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Chime, 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($237K) sits 12% above the category median. Disclosed range: $199K to $275K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Chime AI Hiring
Chime has 4 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span San Francisco, CA, US, San Francisco, CA. Compensation range: $275K - $275K.
Location Context
AI roles in San Francisco pay a median of $263,811 across 2,416 tracked positions. That's 24% 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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