Senior Machine Learning Engineer

Confirmed live yesterday High trust

Quick summary

Work type: On-site
Location: Austin, TXCary, NC
Posted: 7 days ago
Freshness: Confirmed live yesterday
Closes: Jan 26, 2027
Nearby: 99+ roles within 25 mi

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Salary context

How this pay compares to similar roles

Similar $220k

$171k most similar roles pay here $272k

This listing doesn't post a salary. Most similar roles pay $185,243–$254,750.

Based on 240 similar postings.

Employer

About Q2

Q2 Holdings is a cloud-based banking software company providing digital banking solutions to banks, credit unions, and alternative financial companies, including consumer and business banking platforms. Industry: Financial Technology & Digital Banking

Q2 currently has 31 open roles on FindRole.

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At a glance

TL;DR · Senior Machine Learning Engineer

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Senior Machine Learning Engineer joins a fast-growing AI engineering team to build the next generation of intelligent products for the financial services industry. This role involves designing and delivering production-ready AI solutions, developing scalable APIs, microservices, data integrations, and backend systems. The engineer will build infrastructure including evaluation frameworks, deployment pipelines, monitoring, and observability while prototyping emerging technologies like LLMs and agentic workflows to create enterprise-grade solutions. Key technical requirements include proficiency in Python, with valuable experience in C#, Java, or TypeScript. Candidates should possess expertise in cloud infrastructure, containers, Kubernetes, Docker, and MLOps. The role requires building systems for the financial services sector, specifically focusing on integrating AI capabilities into a broader technology ecosystem to solve complex business problems through innovative software engineering and modern AI patterns like RAG and model integration.

What does a Machine Learning Engineer earn?

Median $236000 from 319 postings across 54 companies.

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Skills

Python LLM RAG Agentic Workflows Microservices Kubernetes Docker MLOps Cloud Infrastructure Data Integration Distributed Systems C# Java TypeScript Monitoring Observability

What you'll do

Design and deliver production-ready AI capabilities, services, and applications to solve business problems.
Develop scalable APIs, microservices, data integrations, and backend systems for AI-powered products.
Build and improve infrastructure including evaluation frameworks, deployment pipelines, monitoring, and automated testing.
Prototype and evaluate emerging technologies like LLMs and agentic workflows for enterprise-grade solutions.
Integrate AI capabilities across cloud infrastructure, data platforms, and application layers.
Collaborate with cross-functional teams to define technical solutions and manage trade-offs from exploration to production.

What we're looking for

Bachelor's degree and 5 years of experience, an advanced degree and 3 years of experience, or equivalent relevant work experience.
At least 5 years of professional software, machine learning, or AI engineering experience with production ownership.
Strong software engineering fundamentals for designing, building, deploying, and maintaining production systems.
Proficiency in Python is required, with additional value placed on C#, Java, or TypeScript.
Experience building APIs, backend services, microservices, data integrations, or distributed systems.
Hands-on experience with cloud infrastructure, containers, and MLOps tools like Kubernetes and Docker.
Experience operating ML/AI systems in production including evaluation, monitoring, observability, and deployment pipelines.
Exposure to LLM applications, RAG, agentic workflows, and modern AI patterns.