Senior Staff Engineer, AI Compute

Capital One Financial

Confirmed live yesterday High trust

Remote

Quick summary

Work type: Remote
Location: San Francisco, CACambridge, MARichmond, VAMcLean, VA
Salary: $286,200–$326,700 / yr
Posted: 11 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $221k

This role $306k

$161k most similar roles pay here $344k

This role pays more than 96% of similar roles. Most pay $187,375–$254,750 — the shaded band above. At the midpoint, this role pays about $306k versus about $221k for comparable roles.

Based on 240 similar postings.

Employer

About Capital One Financial

Capital One Financial is a bank holding company specializing in credit cards, auto loans, banking, and savings products, known for its data-driven approach to consumer and commercial finance. Industry: Financial Services & Banking

Capital One Financial currently has 998 open roles on FindRole.

Listed pay typically runs $197,300–$225,100 across 992 roles with salary data.

Most-posted roles

View all roles at Capital One Financial

At a glance

TL;DR · Senior Staff Engineer, AI Compute

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Senior Staff Engineer, AI Compute joins the machine learning platform organization to engineer and scale foundational compute capabilities for cloud-based enterprise systems. This role involves building common infrastructure on CPU and GPU substrates to support developer notebooks, model training, inference, feature generation pipelines, and pre-training or fine-tuning Transformer-based models. The candidate will architect control and data plane implementations, develop Ray and Spark distributed compute engine solutions, and automate operational workflows for high-scale workloads. Key technical requirements include proficiency in Golang, Python, Scala, or Java, along with experience in Spark, Dask, Ray, Flink, Kubernetes, and AWS Lambda. The role addresses the challenge of providing a multi-tenant, secure environment for large-scale data processing and generative AI applications while optimizing compute unit economics across real-time and batch workloads to support complex machine learning infrastructure.

What does a Engineer earn in California?

Median $208000 from 94 postings across 22 companies.

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Skills

Python Golang Spark Ray Flink Kubernetes AWS Lambda EKS EC2 Scala Java Distributed Systems Machine Learning LLM Microservices

What you'll do

Architect and build control and data plane implementations for a multi-tenant, high-scale machine learning platform.
Develop Ray and Spark distributed compute solutions to accelerate LLM pre-training, fine-tuning, and large-scale data processing.
Engineer systemic improvements to automate "Keep The Lights On" workflows and improve operational excellence.
Optimize compute unit economics for diverse workloads including model inference and feature generation pipelines.
Direct the technical execution of a project portfolio involving distributed microservices and foundation models.
Lead system design sessions, code reviews, and technical knowledge sharing across the engineering organization.
Mentor internal talent and actively recruit external candidates to grow the company's technical talent pool.

What we're looking for

A Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or a related field is required.
A Master's degree in Computer Science, AI, Electrical Engineering, or Computer Engineering is preferred.
Candidates with a Bachelor's degree must have at least 10 years of experience developing AI and ML algorithms or technologies.
Candidates with a Master's degree must have at least 8 years of experience developing AI and ML algorithms or technologies.
At least 10 years of experience programming with Python, Go, Scala, or Java is required.
Experience in the internals of Ray or Spark is preferred.
Experience building platforms for LLM training, fine-tuning, or high-throughput inference is preferred.
Hands-on experience with AWS-specific compute primitives and cost-optimization strategies is preferred.

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