Machine Learning Engineer

Hybrid

Quick summary

Work type: Hybrid
Location: Austin, TX
Posted: 46 days ago
Nearby: 99+ roles within 25 mi

Market check

Salary context

How this pay compares to similar roles

Similar $216k

$161k most similar roles pay here $273k

This listing doesn't post a salary. Most similar roles pay $181,587–$249,750.

Based on 239 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 55 open roles on FindRole.

Most-posted roles

View all roles at Q2

At a glance

TL;DR · Machine Learning Engineer

Apply Now Log in to save

As a Machine Learning Engineer at Q2, you will join a dynamic team of innovators to design and implement advanced AI solutions that drive business innovation. Your day-to-day responsibilities include developing machine learning algorithms for diverse applications, conducting research to enhance ML capabilities, and collaborating with cross-functional teams to integrate these solutions into production environments. You will analyze large datasets to derive actionable insights, build scalable pipelines using cloud platforms like TensorFlow or PyTorch, and ensure the quality of AI systems through rigorous testing. This role requires a strong background in statistics, optimization, and programming languages such as Python or R, along with experience in deploying ML models at scale.

Skills

Python TensorFlow PyTorch scikit-learn R Java cloud platforms scalable computing resources machine learning pipelines data analysis statistics optimization probability theory experimental methodologies CI/CD

What you'll do

Design and implement machine learning algorithms for business applications.
Conduct research to advance machine learning capabilities within the company.
Develop scalable machine learning pipelines and systems for production use.
Analyze large datasets to extract meaningful insights supporting data-driven decisions.
Ensure quality and performance of AI systems through rigorous testing.

What we're looking for

5-8 years of experience in machine learning model development and deployment.
Proficiency in Python, R, or Java for ML applications.
Expertise in ML frameworks like TensorFlow, PyTorch, and scikit-learn.
Strong knowledge of statistics, optimization, probability theory, and experimental methodologies.
Ability to analyze large datasets and extract meaningful insights.
Experience with cloud platforms and scalable computing resources.

Hybrid

Save