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About This Role
\*\*To be considered for this position, candidates must be legally authorized to work in the United States on a full\-time basis without the need for employer sponsorship now or in the future.
SavvyMoney is a leading San Francisco East Bay fintech company. We provide integrated credit score and personal finance solutions to 1,600 \+ bank and credit union partners nationally. The SavvyMoney solutions integrate with more than 43 digital banking platforms.
SavvyMoney was recently recognized by the San Francisco Business Times and the Silicon Valley Journal as one of the "Top 25 Places to Work in the San Francisco Bay Area" and is an Inc. 5000 Fastest Growing Company.
Reporting to the VP, Information Security \& DevOps, the Lead, AI Engineer owns both halves of AI at SavvyMoney: the systems and the adoption. You architect and personally write the internal AI tooling the company runs on, and you own getting it used. The work is the same shape as effective security or DevOps platform work — paved roads, policy, telemetry, champions, enforcement, friction reduction, repetition — applied to a new substrate. You are the connective tissue between business stakeholders, engineering, and executive leadership, translating AI capability into shipped tooling and role\-based behavior change across our 200\+ person company and our partner operations.
This is a high\-visibility role inside our newly chartered AI Engineering Team. You won't just lead — you are the most senior builder on the team, and you manage the AI Engineer on it. You spend the majority of your week shipping internal AI tools and reference architectures, and you personally drive the adoption of what you build: literacy, champions, and a culture where AI is a default tool rather than a lighthouse project.
Key Responsibilities
Hands\-On Build and Technical Direction
- Own the technical direction of every internal AI system we run, and write a large share of it yourself.
- Define the reference architectures the whole company builds on — RAG pipelines, agent loops, evals, the LLM gateway, observability, and cost control — and prototype the first working version of each.
- Ship production systems end\-to\-end with the AI Engineer: requirements, prototype, deploy, instrument, iterate.
- Set the technical bar by example — code review, eval coverage, prompt\-injection defense, and cost\-per\-outcome discipline.
Stakeholder Partnership and Delivery
- Run intake with business and executive stakeholders — elicit requirements, pressure\-test the use case, and decide what the team builds, buys, or declines.
- Own the acceptance gate: the stakeholder who requested the work confirms it in UAT before it ships.
- Present outcomes to the people who fund and use them — monthly executive review, quarterly business reviews, and demos to the teams whose work changes.
Champions Program and Community
- Recruit, train, and run a network of named "AI champions" — at least one per business unit — who serve as distributed sensors and accelerators for adoption.
- Run the champions cadence — monthly sync, quarterly offsite, recognition tied to measured impact — and the internal community of practice that shares wins, patterns, and friction across teams.
Training and Literacy
- Design and deploy a scalable AI literacy curriculum with role\-specific tracks for engineering, customer success, finance, legal, sales, recruiting, and partner ops.
- Own build\-vs\-buy on training vendors and certification pathways, and grow a measurable AI\-fluency baseline quarter over quarter.
Communications and Storytelling
- Own the internal AI Slack channel, monthly newsletter, quarterly town halls, and a success\-story library tied to dollarized outcomes — translating complex AI concepts into narratives that land with technical and executive audiences alike.
Office Hours and Friction Removal
- Run weekly drop\-in office hours that make the AI Engineering Team's tools and support accessible to every team.
- Identify recurring friction (policy ambiguity, tool gaps, integration blockers) and partner with your AI Engineer and the VP, Information Security \& DevOps to remove it.
Adoption Telemetry
- Own the data: % active users by team, by tool, by role.
- Identify dark spots and design targeted interventions— such as training, champion deployment, leadership nudges, or licensing changes.
- Report adoption metrics into the monthly executive review and quarterly PSG scorecard.
Policy Rollout and Tool Licensing
- When the AI Engineering Team ships an acceptable\-use policy or adopts a new tool, you own getting it adopted in practice — not just published.
- Advise on which seats go where, based on adoption data and ROI signals rather than headcount.
Partner Ops Enablement
- Extend the champions and training model to partner ops teams where ROI clearly exceeds the cost of a custom build.
- Coordinate with our partner\-facing teams to surface AI use cases that scale across our 1,600\+ FI relationships.
Required Skills and Qualifications
- 5\+ years of professional software engineering experience, including production LLM systems you personally architected and shipped.
- Deep hands\-on proficiency in Python and cloud\-native AWS development, with strong opinions on evals, cost\-per\-outcome, latency, and prompt\-injection defense.
- Deep technical literacy with modern AI tools (Copilot, Cursor, Claude, Glean, ChatGPT) — you use them daily, not just demo them.
- Strong analytical mindset with experience defining adoption metrics, instrumenting telemetry, and reporting to executive audiences.
- Excellent written and verbal communication — you can run a requirements session with a business team and present the outcome to the executive team in the same week.
- Demonstrated success driving organization\-wide behavior change and running a champions network or community of practice at scale (500\+ employees).
- Comfort working cross\-functionally with engineering, legal, security, HR, and business leadership.
Preferred Experience
- Fintech, lending, or financial services background.
- Prior experience in InfoSec, DevOps, or a regulated\-industry technical function — the policy\-and\-telemetry muscle translates directly.
- PE\-portfolio company experience.
- Experience with AI governance frameworks (NIST AI RMF, ISO 42001, or equivalent).
- Bachelor's degree in a relevant field, or compelling self\-taught equivalent.
What You'll Be Measured On
- Internal AI systems shipped to production, and the business outcome each one moved.
- Eval coverage and cost\-per\-outcome across production AI workflows.
- Reference architectures adopted as the default path by engineering teams across SavvyMoney.
- Active adoption percentage.
- Champion engagement (% of named champions actively contributing each month).
- Training completion rate by role.
- Internal NPS on AI tools and on the team's delivery.
- Communications engagement (Slack, newsletter, office hours).
Base Salary
The annual base salary for this position is between $150,000\.00 and $175,000\.00, depending upon geography and experience.
Additionally we provide
- Equity Compensation Package
- Flexible Time Off (FTO) \- take time off as needed to rest and recharge.
- Medical, Dental, Vision – 100% premium paid for employee
- Disability/Life Insurance
- Opportunity for learning and career growth with a top Bay Area technology company
- Reimbursement for remote work setup
- Monthly stipend for phone and internet
- Team building events, culture activities, all hands events
- Paid time off to volunteer and serve the community
- Half day Fridays
- 401k matching contribution
- Beautiful California East Bay offices in Dublin, CA
SavvyMoney’s EEO Statement
SavvyMoney relies on diversity of culture and thought to deliver on our goal of Creative People, Practical solutions serving our client needs, and ensures nondiscrimination in all programs and activities. We continuously seek talented, qualified employees in our operations regardless of race, color, sex/gender, including gender identity and expression, sexual orientation, pregnancy, national origin, religion, disability, age, marital status, citizen status, protected veteran status, or any other protected classification under country or local law. SavvyMoney is proud to be an Equal Employment Opportunity/ Affirmative Action Employer.
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Salary Context
This $150K-$175K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At SavvyMoney, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($162K) sits 24% below the category median. Disclosed range: $150K to $175K.
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.
SavvyMoney AI Hiring
SavvyMoney has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Dublin, CA, US. Compensation range: $110K - $175K.
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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