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Assistant Machine Learning Quant Jobs in Texas (NOW HIRING)

Cisco

Principal Machine Learning Engineer

Austin, TX · On-site

The Principal Machine Learning Engineer will define the vision for AI across platforms, lead the ... Required : • PhD in Computer Science, or related quantitative field, plus 7+ years of industry ...

Cisco

Principal Machine Learning Engineer

Houston, TX · On-site

The Principal Machine Learning Engineer will define the vision for AI across platforms, lead the ... Required : • PhD in Computer Science, or related quantitative field, plus 7+ years of industry ...

Cisco

Principal Machine Learning Engineer

Dallas, TX · On-site

The Principal Machine Learning Engineer will define the vision for AI across platforms, lead the ... Required : • PhD in Computer Science, or related quantitative field, plus 7+ years of industry ...

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Assistant Machine Learning Quant information

What are Assistant Machine Learning Quants?

Assistant Machine Learning Quants are entry-level professionals in quantitative finance who support senior quants by applying machine learning techniques to analyze financial data, build predictive models, and develop trading strategies. Their responsibilities often include data cleaning, feature engineering, model selection, and performance evaluation. They work closely with quantitative researchers and traders to improve algorithmic trading systems and risk management processes. This role typically requires strong programming skills, a solid understanding of machine learning concepts, and familiarity with financial markets.

How does an Assistant Machine Learning Quant typically collaborate with senior quants and data scientists on projects?

As an Assistant Machine Learning Quant, you will often work closely with senior quantitative researchers and data scientists by supporting model development, data preprocessing, and feature engineering tasks. You may contribute to brainstorming sessions, implement prototypes, and assist in backtesting trading strategies or risk models. This collaborative environment provides valuable mentorship opportunities and exposure to best practices in quantitative analysis and machine learning within the finance industry. Effective communication and a willingness to learn from senior team members are key to success in this role.

What are the key skills and qualifications needed to thrive as an Assistant Machine Learning Quant, and why are they important?

To thrive as an Assistant Machine Learning Quant, you need strong quantitative skills, a background in statistics or mathematics, and typically a degree in a STEM field. Familiarity with programming languages such as Python or R, experience with machine learning frameworks, and knowledge of financial modeling tools are essential. Strong problem-solving abilities, attention to detail, and effective communication are standout soft skills in this role. These competencies enable accurate model development, efficient data analysis, and clear collaboration with team members in high-stakes financial environments.
What are the most commonly searched types of Machine Learning Quant jobs in Texas? The most popular types of Machine Learning Quant jobs in Texas are:
What are popular job titles related to Assistant Machine Learning Quant jobs in Texas? For Assistant Machine Learning Quant jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Assistant Machine Learning Quant jobs in Texas look for? The top searched job categories for Assistant Machine Learning Quant jobs in Texas are:
What cities in Texas are hiring for Assistant Machine Learning Quant jobs? Cities in Texas with the most Assistant Machine Learning Quant job openings:
Assistant Machine Learning Quant jobs near you
Infographic showing various Assistant Machine Learning Quant job openings in Texas as of June 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.
Finance Machine Learning Engineer - Tech Lead

Finance Machine Learning Engineer - Tech Lead

Apple

Austin, TX

$101K - $133K/yr

Full-time

Posted 6 days ago

Apple rating

8.1

Company rating: 8.1 out of 10

Based on 662 frontline employees who took The Breakroom Quiz

6th of 30 rated technology retailers

Job description

Imagine what you could do here. At Apple, new ideas have a way of becoming great products, services, and customer experiences very quickly. Bring passion and curiosity to your job and there's no telling what you could accomplish. Do you love thinking analytically? Just as our customers find value in Apple products, the Finance group finds value for both Apple and its shareholders. As a machine learning engineer in Finance, you’ll play an integral role in building the data foundations, services, and platforms used for delivering insights and automating decisions for Apple’s Finance organization.
Description
This role will be the technical lead for product cost, supporting our Operations Finance organization. You will work as part of a multi-discipline engineering pod with data and software engineers, product managers and program managers. Your ability to learn business processes and instill strong engineering practices into team machine learning processes will be critical. A key part of your role will be to operationalize AI solutions, bridging the gap between prototype and production to rapidly and reliably deliver value to the Finance organization.","responsibilities":"Technical lead overseeing solution design and engineers
Partner with teammates and share expertise across teams
Explain technical concepts to non-technical audiences
Collaborate effectively with cross-functional teams
Operationalize AI solutions, bridging the gap between prototype and production
Instill strong engineering practices into team machine learning processes
Rapidly and reliably deliver value to the Finance organization
Preferred Qualifications
Previous experience working in a corporate finance, accounting, or supply chain organization
Understanding of or ability to learn financial statements, P&L impact, high level accounting principles, SOX and tax compliance and month-end close process
Minimum Qualifications
At least 8 years experience in an engineering role
At least one year experience effectively leading engineers and collaborating cross-functionally, translating technical concepts for diverse audiences and converting ideas into solutions with strong process and data understanding
Experience building data models and scalable pipelines using SQL and big data technologies, with expertise in data ops best practices
Experience developing in Python while following and advocating for DRY principles, modularity, testing standards, version control, and code reviews. Experience with front end (.js experience)
Experience applying ML algorithms for regression, classification, and anomaly detection; build generative AI and agentic solutions; implement MLOps/LLMOps including CI/CD, drift monitoring, and familiarity working with cloud platforms (AWS, GCP, Azure)
Graduate degree (computer science, data science, math, quantitative finance, or similar discipline)
Undergraduate degree (computer science, data science, finance, economics, accounting, or related business discipline)

What Apple employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

Apple logo

About Apple

Sourced by ZipRecruiter

Imagine what you could do here! At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Dynamic, intelligent people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same real passion for innovation that goes into our products also applies to our practices strengthening our dedication to leave the world better than we found it.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Cupertino, CA, US

Year founded

1976

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