Interested in this AI/ML Engineer role at Capital One?
Apply Now →Skills & Technologies
About This Role
Sr Manager IC, FinOps \& AI Enablement — Analytics Team (Remote \- Eligible)The role
Own the cost and AI\-leverage layer of the analytics platform behind Capital One Shopping — the systems between petabyte\-scale data and the humans and tools that query it. This is an own\-and\-build role, not a maintenance seat: you own the production platforms below, and in your first six months you ship three net\-new systems on top of them. You'll report to the Engineering Director for the Shopping data platform as one of two senior IC pillars of the analytics org.
What you own
You own, support, and evolve three production platforms — ideation through implementation to production support — and you're the SME and mentor for the analysts, BAs, and engineers who use them:
- A data warehousing platform serving \~250K queries/day over \~20PB.
- An event ingestion pipeline taking in 6–7 billion events/day.
- The Airflow orchestration platform.
You own the technology choices and the strategic backlog and priorities for this surface — and you carry ongoing production support and an on\-call rotation for these platforms and the models on them. You build the three net\-new systems below on top of that operational base.
What you'll build
- A cost\-attribution pipeline that parses Trino query logs at production traffic and attributes real AWS dollars to every report, query, user, and dbt model — reconciled against a \~$750K/month cloud bill.
- A forecast\-driven autoscaling control loop for the shared Trino cluster and dbt worker pool — turning today's event\-only Nomad autoscaler (Prime Day, Cyber Week) into steady\-state, forecast\-driven capacity. The single largest lever on the analytics AWS bill.
- A production Gen AI system — natural\-language\-to\-SQL or RAG over the data catalog — with real LLM tool\-use, grounding, and cost guardrails, adopted by internal teams.
The stack
Kafka streaming backbone into an S3 lakehouse (Hive \+ Iceberg), Cassandra, Postgres, DynamoDB, ElasticSearch, Aurora MySQL. Queried through Trino/Presto and Spark SQL, modeled in dbt, orchestrated on Airflow and Nomad and containers (Docker/Kubernetes), on a deep AWS footprint. SQL and Python daily; Go, Java, and TypeScript/JavaScript across the surrounding platform.
The day\-to\-day
"Manager" is the level, not the job — this is an individual\-contributor role, and you'll spend most of your day hands\-on in the editor. Roughly 70% building: writing the log\-parsing and cost\-attribution logic and its dbt models, building and tuning the forecast\-driven autoscaler control loop, and building the RAG / natural\-language\-to\-SQL system yourself. The other \~30% is technical coordination — reconciling your cost numbers with Finance, the R\&D memo, aligning report owners — not status decks or people\-management. Daily rhythm is multi\-terminal Claude Code: query\-log analysis in one, dbt work in another, AI iteration in a third. No direct reports — you build.
What we're looking for
- 10\+ years engineering experience owning and supporting mission\-critical applications and platforms in production
- Deep experience with Kafka and streaming technologies and platforms
- Experience with enterprise data technologies and platforms
- A track record working with extremely large traffic and data volumes
- Fluency across a real stack: JavaScript, Java, HTML/CSS, TypeScript, SQL, Python, and Go, open\-source RDBMS and NoSQL databases, container orchestration (Docker and Kubernetes), and a broad range of AWS tools and services
- Forecast\- or workload\-driven infrastructure scaling on a shared platform — you've scaled shared infra up and down against a forecast, not just event\-driven bursts
- Production Gen AI — RAG design, vector/graph stores, LLM tool\-use — on top of a *longer* ML/AI arc (ranking, recommendation, or comparable production ML predating the 2023 Gen AI boom)
- 0\-to\-1 delivery of a product that booked measurable revenue or adoption in its first weeks, plus a lead\-engineer role modernizing an ingest pipeline off a legacy provider onto a cloud\-native stack
- Experience directing a cross\-team migration or deprecation at senior\-executive scope to completion
- A broad background and a genuinely fast learner — the stack keeps moving
- Comfortable working with large teams on large\-scale systems, and cross\-functionally (weekly Finance/BizOps and Analytics cadences)
- Claude Code fluency — daily use, skill authoring, PR\-level deliverables (hard requirement)
Basic Qualifications:
- Bachelor’s Degree
- At least 6 years of experience in software engineering (Internship experience does not apply)
- At least 1 year experience with cloud computing (AWS, Microsoft Azure, Google Cloud)
Preferred Qualifications:
- Master’s Degree
- 9\+ years of experience in at least one of the following: JavaScript, Java, TypeScript, SQL, Python, or Go
- 4\+ years of experience with AWS, GCP, Microsoft Azure, or another cloud service
- 4\+ years of experience in open source frameworks
- 1\+ years of people management experience
- 2\+ years of experience in Agile practices
*Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.*
The minimum and maximum full\-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part\-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.
Remote (Regardless of Location): $209,000 \- $238,500 for Sr. Lead Software Engineer
McLean, VA: $229,900 \- $262,400 for Sr. Lead Software Engineer
Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate’s offer letter.
This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.
Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well\-being. Learn more at the Capital One Careers website. Eligibility varies based on full or part\-time status, exempt or non\-exempt status, and management level.
This role is expected to accept applications for a minimum of 5 business days.
No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non\-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug\-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23\-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901\-4920; New York City’s Fair Chance Act; Philadelphia’s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.
If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1\-800\-304\-9102 or via email at [email protected]. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.
For technical support or questions about Capital One's recruiting process, please send an email to [email protected]
Capital One does not provide, endorse nor guarantee and is not liable for third\-party products, services, educational tools or other information available through this site.
Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
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 Capital One, 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.
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.
Capital One AI Hiring
Capital One has 24 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Data Scientist, AI Software Engineer. Positions span McLean, VA, US, New York, NY, US, Plano, TX, 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.