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Senior Machine Learning Engineer Jobs in Texas (NOW HIRING)

CesiumAstro

Senior Machine Learning Engineer II

Austin, TX

$103K - $142K/yr

We are looking for a Senior Machine Learning Engineer II to contribute to the development and deployment of machine learning solutions for our advanced distributed processing platforms. This role is ...

Toyota

Sr. Machine Learning Engineer

Plano, TX · On-site

$117K - $154K/yr

Toyota's Data Science department is looking for a passionate and highly motivated Machine Learning Engineer. The primary responsibility of this role is to operationalize complex models, analytical ...

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Compunnel

Machine Learning Engineer

Plano, TX · On-site

Job Summary We are seeking a Machine Learning Engineer with strong expertise in machine learning model development, data engineering, and modern cloud-based analytics platforms. This role will focus ...

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Senior Machine Learning Engineer information

See Texas salary details

$55.4K

$117.9K

$171K

How much do senior machine learning engineer jobs pay per year?

As of Jun 17, 2026, the average yearly pay for senior machine learning engineer in Texas is $117,907.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,400.00 and $133,700.00 per year, depending on experience, location, and employer.

What are some common challenges Senior Machine Learning Engineers face when deploying models to production, and how can they be addressed?

Senior Machine Learning Engineers often encounter challenges related to model scalability, maintaining performance in real-world scenarios, and ensuring reliable integration with existing systems. Addressing these challenges typically involves thorough testing, implementing robust monitoring for model drift, and collaborating closely with DevOps and software engineering teams to streamline deployment pipelines. Staying updated on best practices in MLOps and adopting tools for automated deployment and monitoring can greatly improve the reliability and efficiency of production models.

What does a Senior Machine Learning Engineer do?

A Senior Machine Learning Engineer designs, develops, and implements machine learning models to solve complex problems. They are responsible for selecting appropriate algorithms, preprocessing data, and optimizing model performance. Additionally, they collaborate with data scientists, software engineers, and product teams to integrate machine learning solutions into production systems. Senior engineers also mentor junior team members and contribute to setting technical direction for machine learning projects.

What are the key skills and qualifications needed to thrive as a Senior Machine Learning Engineer, and why are they important?

To thrive as a Senior Machine Learning Engineer, you need advanced knowledge of machine learning algorithms, statistical modeling, and programming languages like Python or Java, typically supported by a degree in computer science or a related field. Experience with frameworks and tools such as TensorFlow, PyTorch, scikit-learn, and cloud platforms, as well as familiarity with version control and CI/CD systems, is essential. Strong problem-solving, communication, and leadership skills help you collaborate effectively and mentor junior team members. These capabilities are crucial for designing scalable ML solutions and driving impactful results within complex, dynamic projects.

What is the difference between Senior Machine Learning Engineer vs Data Scientist?

AspectSenior Machine Learning EngineerData Scientist
Required CredentialsBachelor's/Master's in CS, ML, or related; experience with ML frameworksBachelor's/Master's in CS, Statistics, or related; strong analytical skills
Work EnvironmentDevelops and deploys ML models in production systemsAnalyzes data, builds models, and provides insights
Industry UsageTech, finance, healthcare, e-commerceResearch, finance, marketing, tech

While both roles require strong technical skills and knowledge of machine learning, Senior Machine Learning Engineers focus more on deploying scalable ML solutions in production environments, whereas Data Scientists primarily analyze data and develop models for insights. The roles often overlap but differ in their core responsibilities and focus areas.

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Senior Machine Learning Engineer jobs near you
Senior Machine Learning Engineer, Wallet, Payment & Commerce

Senior Machine Learning Engineer, Wallet, Payment & Commerce

Apple

Austin, TX

$103K - $142K/yr

Other

Posted yesterday

Apple rating

8.1

Company rating: 8.1 out of 10

Based on 661 frontline employees who took The Breakroom Quiz

6th of 30 rated technology retailers

Job description

Are you motivated by providing software security technologies to help users protect their accounts and provide the best customer experience? Are you a Machine Learning Engineer who enjoys crafting, implementing and operating analytical solutions? \\n\\nIf so, we invite you to come and join the Apple Wallet, Payment & Commerce team in transforming the smartphone into a device that secures the user's digital life without sacrificing privacy!\\n
Our team employs predictive modeling and statistical analysis techniques and builds end-to-end solutions for improving security, fraud prevention, and operational efficiency across Apple. Our team collaborates cross-functionally with engineering teams across the company. Apple's dedication to customer privacy, the adversarial nature of fraud, and the enormous scale of the business present exciting challenges to traditional machine learning and data science techniques.
Master's degree in Computer Science, Statistics, Machine Learning, or equivalent field (e.g., Business Analytics with quantitative focus).\nAt least five years of industry experience deploying machine learning algorithms - including classification, clustering, and anomaly detection - to support customer-facing features in production environments.\nDeep expertise working with relational databases and SQL, and large-scale distributed computing systems such as Hadoop and Spark.\nStrong programming skills in one or more of the following languages: Python, Scala, or Java; familiarity with Objective-C or Swift for on-device model deployment contexts.\nExperience with ML workflow and data management tooling, including workflow orchestration frameworks (e.g., Airflow), distributed compute frameworks (e.g., Ray), experiment tracking platforms (e.g., Weights & Biases), and ML model development frameworks (e.g., Turi Create).\nExperience implementing privacy-preserving techniques on production data pipelines and ML models across multiple projects.\nExperience in data acquisition program management, including working with external vendors and procurement teams, and designing and executing user studies to build high-quality labeled datasets.\nDomain expertise in fraud detection, risk modeling, or security-focused machine learning applications.
Experience with the secure handling, processing, and governance of sensitive personal data in production ML systems.\nExperience integrating device-based signals and features into risk models, including identification of device-based fraud risk indicators.\nPrior experience with Institutional Review Board (IRB) processes, informed consent frameworks, and the design and execution of user studies for data collection purposes.\nDemonstrated history of measurable business impact through fraud prevention with minimal disruption to the legitimate customer experience.\nFamiliarity with internal datasets, tooling, and systems relevant to payments, Wallet, and fraud decisioning.

What Apple employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

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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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