1

Graduate Machine Learning Engineer Jobs in Texas

Machine Learning Engineer II

Plano, TX · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

As a Machine Learning Engineer, you will play a crucial role in developing and deploying cutting-edge machine learning models and solutions to enhance various aspects of our business operations, from ...

Machine Learning Engineer

Austin, TX · On-site

$199K - $331K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

Engineers on the BCI team utilize signal processing and machine learning to communicate with the brain. You will have access to the most cutting-edge neural interface hardware and develop ...

Machine Learning Engineer

Austin, TX · On-site

$199K - $331K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

Engineers on the BCI team utilize signal processing and machine learning to communicate with the brain. You will have access to the most cutting-edge neural interface hardware and develop ...

Machine Learning Engineer II

Plano, TX

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

As a Machine Learning Engineer, you will play a crucial role in developing and deploying cutting-edge machine learning models and solutions to enhance various aspects of our business operations, from ...

Machine Learning Engineer II

Plano, TX

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

As a Machine Learning Engineer, you will play a crucial role in developing and deploying cutting-edge machine learning models and solutions to enhance various aspects of our business operations, from ...

Engineers on the BCI team utilize signal processing and machine learning to communicate with the brain. You will have access to the most cutting-edge neural interface hardware and develop ...

Machine Learning Engineer

Taylor, TX · On-site

$90 - $175/hr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Position Summary As a Machine Learning Engineer at Samsung Austin Semiconductor, you will build and maintain the model pipelines for our anomaly detection and root cause analysis systems. You will ...

New

Machine Learning Engineer

Austin, TX · On-site

$170K - $250K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Your Job The ML Engineer will build physics-informed surrogate models on Azure Machine Learning that predict engineering simulation outcomes directly from design parameters. They will be pre ...

New

Senior Machine Learning Engineer

Austin, TX · On-site

$121K - $160K/yr

... Machine Learning Engineer. This role will assist our Online Retail Decision Automation team by ... Preferred Qualifications PhD or Graduate degree with research/work experience using data science ...

Showing results 21-40

Graduate Machine Learning Engineer information

See Texas salary details

$29.3K

$120K

$180.3K

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

As of Aug 20, 2026, the average yearly pay for graduate machine learning engineer in Texas is $119,968.00, according to ZipRecruiter salary data. Most workers in this role earn between $94,600.00 and $144,400.00 per year, depending on experience, location, and employer.

What does a graduate machine learning engineer do?

A Graduate Machine Learning Engineer is an entry-level professional who designs, develops, and tests machine learning models and algorithms. They work with data scientists and engineers to preprocess data, train models, and deploy solutions to solve real-world problems. Their responsibilities often include coding in languages like Python, using libraries such as TensorFlow or PyTorch, and staying updated with the latest advancements in machine learning. This role serves as a starting point for a career in AI, providing hands-on experience in building and optimizing intelligent systems.

What are some common challenges faced by graduate machine learning engineers during their first year, and how can they overcome them?

Graduate Machine Learning Engineers often encounter challenges such as bridging the gap between academic knowledge and real-world application, working with large or messy datasets, and learning to collaborate within cross-functional teams. Adapting to production-level code standards and understanding existing codebases can also be demanding. To overcome these hurdles, it's helpful to seek mentorship from experienced colleagues, actively participate in code reviews, and invest time in learning best practices for data preprocessing and model deployment. Embracing continuous learning and open communication will ease the transition into the professional environment.

What are the key skills and qualifications needed to thrive as a graduate machine learning engineer, and why are they important?

To thrive as a Graduate Machine Learning Engineer, you need a solid foundation in computer science, mathematics (especially statistics and linear algebra), and proficiency in programming languages like Python, often supported by a relevant degree. Familiarity with machine learning frameworks (such as TensorFlow or PyTorch), version control systems (like Git), and experience with cloud platforms or data management tools are typically expected. Strong analytical thinking, problem-solving abilities, and effective communication help you collaborate and translate complex concepts into practical solutions. These skills and qualities are crucial for developing robust models, integrating them into real-world applications, and contributing effectively to multidisciplinary teams.

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

AspectGraduate Machine Learning EngineerData Scientist
Required CredentialsBachelor's or Master's in CS, Data Science, or related field; some internshipsBachelor's or Master's in Statistics, Data Science, or related field; often with experience
Work EnvironmentDeveloping ML models, coding, testing algorithmsAnalyzing data, creating visualizations, deriving insights
Employer & Industry UsageTech companies, startups, research labsFinance, healthcare, tech, consulting firms

While both roles involve working with data and algorithms, Graduate Machine Learning Engineers focus on developing and deploying machine learning models, often requiring coding and technical skills. Data Scientists analyze data to extract insights and inform decisions. The roles overlap in skills but differ in primary responsibilities and focus areas.

Infographic showing various Graduate Machine Learning Engineer job openings in Texas as of August 2026, with employment types broken down into 1% As Needed, 78% Full Time, 20% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $119,968 per year, or $57.7 per hour.

Machine Learning Engineer

Dow - THE DOW CHEMICAL COMPANY

Houston, TX • On-site

$109K - $131K/yr

Full-time

Medical, Life, Retirement, PTO

Posted 20 days ago


Job description

At Dow, we believe in putting people first and we're passionate about delivering integrity, respect and safety to our customers, our employees and the planet.

Our people are at the heart of our solutions. They reflect the communities we live in and the world where we do business. Their diversity is our strength. We're a community of relentless problem solvers that offers the daily opportunity to contribute with your perspective, transform industries and shape the future. Our purpose is simple - to deliver a sustainable future for the world through science and collaboration.If you're looking for a challenge and meaningful role, you're in the right place.

About you and this role

Dow has an exciting and challenging opportunity for a Machine Learning Engineer within our Enterprise Data & Analytics organization located in Houston, TX; Midland, MI; or Champaign, IL.


As a Machine Learning Engineer in our Data & Analytics Platforms team specializing in data and analytics solutions, you will work with a cross-functional team whoseobjectiveis todeliversolutions that drive business valuefor Dow. In this role, you will work closely with data engineers, data scientists, domain experts, and software engineers to design, develop, and deploy machine learning systems, including training and inference pipelines on the Azure Databricks platform. You will also help drive a culture around setting standards and adopting best practices for machine learning and MLOps across the organization.

Responsibilities

  • Designs and implements pipelines and other workflow infrastructure to meet the requirements for new AI/ML solutions that involve online, batch, or real-time inference

  • Deploys and monitors machine learning models in production using Databricks Model Registry, Jobs, and Workspace

  • Frequently collaborates with data engineers, DevOps/platform engineers, data scientists, and domain experts as part of a comprehensive MLOps framework to ensure AI/ML solutions are performant, reliable, and maintainable

  • Works frequently with application development teams to ensure seamless integrations

  • Works proficiently with various ML frameworks, such as scikit-learn, TensorFlow, PyTorch, Keras, as well as distributed frameworks like Spark MLlib and Ray

  • Performs data analysis, feature engineering, model selection, hyperparameter optimization, and model evaluation using Databricks MLFlow, Delta Lake, and SQL Analytics and other tools as part of the end-to-end ML lifecycle

  • Researches and implements new machine learning techniques and methods using Databricks, staying abreast of the latest trends and technologies

  • Documents and communicates machine learning results and insights to stakeholders using Databricks notebooks and dashboards

  • Understands IT security policies and implements them as part of new solution designs

  • Follows and promotes the best practices and standards for machine learning and MLOps across the organization using Databricks and Azure DevOps

Your Skills

  • Solutions Delivery: End-to-end ownership of Azure data solutions-translating ambiguous business needs into robust architectural blueprints, then delivering through design, build, test, deployment, and postlaunch monitoring with a focus on reliability, performance, and cost efficiency.

  • Cloud Computing: Designing and operating cloudnative architectures on Azure (e.g., Databricks Lakehouse, ADF/Workflows, Functions, Logic Apps, Azure SQL) with CI/CD and IaC to scale securely and economically for ML, BI, streaming, and web applications.

  • Integration Services: Building secure, highthroughput integrations-REST APIs and event/stream pipelines (e.g., Event Hubs/Kafka)-to connect polyglot data stores (SQL Server, Cosmos DB, Neo4j) and enable realtime and batch data products.

  • Security Awareness: Embedding governance, identity, and compliance into the architecture (OAuth/RBAC, data governance, reauthorization/ownership verification), enforcing code reviews and automated controls across pipelines and deployments.

  • Strategic Planning: Aligning platform roadmaps and technology choices with enterprise strategy, mentoring engineers on best practices, defining standards and KPIs, and prioritizing modernization and costoptimization initiatives that maximize business impact.

Qualifications

  • A minimum of a Bachelor's degree, or 8 years relevant experience, or relevant military experience at an E6 rank/Petty Officer 2nd Class or higher is required

  • A minimum of 3 years of experience developing solutions in machine learning, data science, or related field

  • A minimum requirement for this U.S. based position is the ability to work legally in the United States. No visa sponsorship/support is available for this position, including for any type of U.S. permanent residency (green card) process

Preferred qualifications

  • A degree in computer science, engineering, mathematics, statistics, data science or related field

  • Proficient in Python and one or more machine learning frameworks such as TensorFlow, PyTorch, Scikit-learn, etc

  • Experience in developing and deploying machine learning models and pipelines on the Databricks platform using Databricks MLflow, Delta Lake, SQL Analytics, Model Registry, Jobs, and Workspace

  • Strong knowledge of machine learning concepts, techniques, and algorithms

  • Ability to perform data analysis, feature engineering, model selection, optimization, and evaluation using Databricks

  • Ability to communicate complex machine learning concepts and results to technical and non-technical audiences using Databricks notebooks and dashboards

  • Ability to work independently and collaboratively in a fast-paced and dynamic environment

  • Curiosity and passion for learning new machine learning skills and technologies using Databricks

  • Strong knowledge of data modeling, data warehousing, and ETL processes

  • Experience designing and deploying into production both traditional and generative AI systems

  • Proficiency in SQL and experience with big data technologies such as Apache Spark and Hive

  • Experience working within Azure Machine Learning

  • Experience containerizing and deploying ML models to Azure Kubernetes Service

  • Experience with Azure Data Factory, Azure Data Lake Storage Gen2, and other Azure services

  • Multi-application and cross-platform design experience

  • Understanding of data lakehouse platform design and associated workflows

  • Ability to thrive in challenging situations and solve complex problems

  • Ability to manage own work effort in multiple projects with little supervision

  • Interested in emerging technologies with the ability to quickly learn and exploit cutting edge offerings to achieve business objectives

Additional notes

  • This position does not offer relocation assistance

  • This position does not have people leadership responsibility. This position is an Independent Contributor; however, you may act as coach and mentor to junior resources

Benefits - What Dow offers you

We invest in you.

Dow invests in total rewards programs to help you manage all aspects of you: your pay, your health, your life, your future, and your career.You bring your background, talent, and perspective to work every day. Dow rewards that commitment by investing in your total wellbeing.

Here are just a few highlights of what you would be offered as a Dow employee:

  • Equitable and market-competitive base pay and bonus opportunity across our global markets, along with locally relevant incentives.
  • Benefits and programs to support your physical, mental, financial, and social well-being, to help you get the care you need...when you need it.
  • Competitive retirement program that may include company-provided benefits, savings opportunities, financial planning, and educational resources to help you achieve your long term financial-goals.
    • Employee stock purchase programs (availability varies depending on location).
  • Student Debt Retirement Savings Match Program (U.S. only).
    • Dow will take the value of monthly student debt payments and apply them as if they are contributions to the Employees' Savings Plan (401(k)), helping employees reach the Company match.
  • Robust medical and life insurance packages that offer a variety of coverage options to meet your individual needs. Travel insurance is also available in certain countries/locations.
  • Opportunities to learn and grow through training and mentoring, work experiences, community involvement and team building.
  • Workplace culture empowering role-based flexibility to maximize personal productivity and balance personal needs.
  • Competitive yearly vacation allowance.
  • Paid time off for new parents (birthing and non-birthing, including adoptive and foster parents).
  • Paid time off to care for family members who are sick or injured.
  • Paid time off to support volunteering and Employee Resource Group's (ERG) participation.
  • Wellbeing Portal for all Dow employees, our one-stop shop to promote wellbeing, empowering employees to take ownership of their entire wellbeing journey.
  • On-site fitness facilities to help stay healthy and active (availability varies depending on location).
  • Employee discounts for online shopping, cinema tickets, gym memberships and more.
  • Additionally, some of our locations might offer:
    • Transportation allowance (availability varies depending on location)
    • Meal subsidiaries/vouchers (availability varies depending on location)
    • Carbon-neutral transportation incentives e.g. bike to work (availability varies depending on location)

Join our team, we can make a difference together.

About Dow
Dow (NYSE: DOW) is one of the world's leading materials science companies, serving customers in high-growth markets such as packaging, infrastructure, mobility and consumer applications.Our global breadth, asset integration and scale, focused innovation, leading business positions and commitment to sustainability enable us to achieve profitable growth and help deliver a sustainable future. We operate manufacturing sites in 30countries and employ approximately36,000 people. Dow delivered sales of approximately$43 billionin 2024. References to Dow or the Company mean Dow Inc. and its subsidiaries. Learn more about us and our ambition to be the most innovative, customer-centric, inclusive and sustainable materials science company in the world by visitingwww.dow.com.

As part of our dedication to inclusion, Dow is committed to equal opportunities in employment. We encourage every employee to bring their whole self to work each day to not only deliver more value, but also have a more fulfilling career. Further information regarding Dow's equal opportunities is available on www.dow.com.

Dow is an Equal Employment Opportunity employer and is committed to providing opportunities without regard for race, color, religion, sex, including pregnancy, sexual orientation, or gender identity, national origin, age, disability and genetic information, including family medical history. We are also committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If you need assistance or an accommodation due to a disability, you may call us at 1-833-My Dow HR (833-693-6947) and select option 8.