Interested in this AI/ML Engineer role at Dave?
Apply Now →Skills & Technologies
About This Role
Dave vs. Goliath. We’re Dave.
=================================
Dave is a financial app on a mission to build products that level the financial playing field. It is redefining the financial landscape by leveraging technology to create an affordable, transparent, and user\-centric access to liquidity for millions of Americans. As a leading innovator in the U.S. financial services sector, Dave’s digital financial platform offers products designed to meet the credit needs of those underserved by traditional financial institutions. Dave’s offerings include its flagship ExtraCash product, providing members up to $500 within minutes. The company is on track to launch several new product offerings in 2026, including a Buy Now Pay Later (BNPL) option.
Dave is focused on serving Americans who are financially vulnerable or living paycheck to paycheck. Dave is leading the charge in creating a new era of credit products that prioritizes speed, affordability, and accessibility, making it the go\-to financial partner for those who need it most.
We’re hiring a Senior IT Systems Engineer to help design and scale how we manage compliance, security, and audit readiness at Dave. This role focuses on building systems and automation that make compliance continuous, reliable, and embedded into how we operate.
The Opportunity
-------------------
You’ll operate at the intersection of IT, security, and compliance, and you’ll be an influential contributor for a team that helps design systems to replace manual audit work with automated, scalable solutions. This includes automating evidence collection, strengthening IT General Controls, and integrating tools so compliance becomes part of day\-to\-day workflows rather than a periodic exercise.
This is a high\-impact role with real ownership. You’ll help shape how we scale our internal process through better systems, working across teams to turn requirements into durable, low\-friction solutions.
What You’ll Build \& Own
----------------------------
- Automate or optimize high\-volume manual processes (audit evidence collection, data gathering, compliance reporting) using APIs and workflows to reduce manual effort and human error
- Build integrations that pull data from systems of record (Okta, Workspace, GitHub, Security tools, Google Cloud, Snowflake) and consolidate it in BigQuery for audit readiness and analytics
- Design and implement agentic workflows that allow non\-engineering stakeholders to interact with company data and reports through conversational interfaces, reducing their need to manually query or compile information
- Own the operational lifecycle of integrations: monitoring, alerting, troubleshooting, and evolving them as business requirements change
- Identify manual handoffs and repetitive tasks across IT, audit, and business functions, then build automation or agent\-driven solutions to eliminate them
- Work cross\-functionally with business teams to understand pain points, translate them into technical solutions, and measure the impact on time savings and compliance readiness
- Build scalable, resilient, and secure systems
The Impact
--------------
Your work will enable Dave to scale securely with greater confidence, reducing audit overhead while increasing trust in our systems and controls.
What We’re Looking For
--------------------------
Experience \& Technical Foundation
- 5–7 years of experience in IT systems engineering, automation, internal web services or a related technical role
- Proficiency in scripting or programming (e.g., Python) for building integrations and automation
- Experience building agents and end\-to\-end automation across systems using APIs, scripting, or workflow platforms
- Hands\-on experience with GCP (Compute Engine, Cloud Functions, Cloud Run, BigQuery, Cloud Storage) and understanding infrastructure as code (e.g. terraform)
- Hands\-on experience with GitHub or equivalent version control, including CI/CD automation (GitHub Actions, automated testing, deployments)
- Strong foundation in consuming and integrating APIs: handling authentication, rate limits, retries, and failures in multi\-system flows
- Experience integrating identity systems (Okta), security tooling, and SaaS platforms into cohesive workflows
- Observability and runbook writing: designing monitoring, logging, and operational documentation so systems can be maintained at scale
- Experience working with stakeholders to gather requirements and informing buy vs build decisions, including scoping POC’s with vendors
- Experience with LLM API’s (ChatGPT, Claude, Gemini)
Bonus
- Familiarity with compliance frameworks (SOC 2, SOX, ITGC, GDPR) and how to build systems that support audit and control requirements
- Experience with data pipelines and BigQuery ELT patterns, schema design, data validation, and understanding how data flows to business users and reporting
- Knowledge of encryption, and PII handling in the context of compliance and data governance
What Makes Someone Successful Here
--------------------------------------
You take responsibility for outcomes and think beyond individual controls to how systems should work end\-to\-end. You care about building solutions that hold up over time, and you’ll reduce manual effort while improving accuracy and trust in our systems.
You work well across boundaries, partnering with Business \& Engineering functions to drive business value with tools. As requirements evolve, you’re comfortable navigating ambiguity, asking thoughtful questions, and adjusting your approach while continuing to move work forward.
What to Expect
------------------
This is a hands\-on role with meaningful ownership and influence. You’ll balance near\-term audit and compliance needs with longer\-term system design, helping us move toward more automated and continuous approaches to compliance.
Why Join Dave
-----------------
- Help redefine how compliance is implemented through systems and automation
- Work on problems that connect security, IT, and the broader business
- Build solutions that scale with the company and reduce operational overhead
- Have real ownership in shaping a more efficient, modern compliance function
Ready to build for the underdog?
Reports to: Manager, Systems Engineering
*Don’t let imposter syndrome get in the way of an incredible opportunity. We’re looking for people who can help us achieve our mission and vision, not just check off the boxes. If you’re excited about this role, we encourage you to apply. You may just be the right candidate for this or other roles.*
Why you’ll love working here:
At Dave, our people are just as important as our product. Our culture reflects the values that guide who we are, how we work, and what we aspire to be. Daves are member\-centric, helpful, transparent, persistent, and better together. We strive to create an environment where all Daves feel valued, heard, and empowered to do their best work. As a virtual\-first company, team members can live and work anywhere in the United States, except Hawaii.
A few of our benefits \& perks:
Opportunity to tackle tough challenges, learn and grow from fellow top talent, and help millions of people reach their personal financial goals
Flexible hours and virtual\-first work culture with a home office stipend
Premium Medical, Dental, and Vision Insurance plans
Generous paid parental and caregiver leave
401(k) savings plan with matching contributions
Financial advisor and financial wellness support
Flexible PTO and generous company holidays, including Juneteenth and Winter Break
All\-company in\-person events once or twice a year and virtual events throughout to connect with your team members and leadership team
Dave Operating LLC is proud to be an Equal Employment Opportunity employer and is dedicated to cultivating a diverse and inclusive workplace. We will consider for employment all qualified applicants and do not discriminate on any basis protected by federal, state, or local law, including the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance relating to an applicant's criminal history.
\#LI\-REMOTE
Compensation Range: $150K \- $187K
Salary Context
This $150K-$187K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Dave, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($168K) sits 7% below the category median. Disclosed range: $150K to $187K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Dave AI Hiring
Dave has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $187K - $187K.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.