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New Grad Machine Learning Jobs in Haslet, TX (NOW HIRING)

Geico

Director, Machine Learning Engineering

Dallas, TX · On-site

$150K - $300K/yr

At this time, GEICO will not sponsor a new applicant for employment authorization for this position. The GEICO Pledge: Great Company: Protecting customers through life's twists and turns with ...

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All JobsNew Grad Machine Learning JobsNew Grad Machine Learning Jobs in Haslet, TX

New Grad Machine Learning information

See Haslet, TX salary details

$24.1K

$40.2K

$83K

How much do new grad machine learning jobs pay per year?

As of Jun 10, 2026, the average yearly pay for new grad machine learning in Haslet, TX is $40,180.00, according to ZipRecruiter salary data. Most workers in this role earn between $30,700.00 and $43,400.00 per year, depending on experience, location, and employer.

What are some typical challenges new graduates might face when starting out in a machine learning role, and how can they overcome them?

New grad machine learning engineers often encounter challenges such as bridging the gap between academic knowledge and practical, production-level projects. Adapting to real-world data issues, collaborating with cross-functional teams, and understanding scalable deployment can be daunting at first. To overcome these, it's helpful to seek mentorship, proactively ask questions, and dedicate time to learning best practices in code versioning, model evaluation, and team communication. Engaging in code reviews and participating in team discussions can also accelerate the learning curve and foster professional growth.

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

To thrive as a New Grad Machine Learning Engineer, you need a solid foundation in mathematics, statistics, and programming (especially Python), typically supported by a degree in computer science or a related field. Familiarity with machine learning frameworks such as TensorFlow or PyTorch, version control systems like Git, and coursework or certification in data science are highly beneficial. Strong problem-solving abilities, curiosity, and effective communication skills help you collaborate and convey complex technical concepts to diverse teams. These skills and qualities are essential for developing innovative models, ensuring project success, and integrating seamlessly into fast-paced tech environments.

What is the difference between New Grad Machine Learning vs Data Scientist?

AspectNew Grad Machine LearningData Scientist
Required CredentialsBachelor's in CS, Data Science, or related field; some internshipsBachelor's or Master's in CS, Statistics, or related; some experience
Work EnvironmentEntry-level, team-focused, research and developmentData analysis, modeling, cross-functional collaboration
Employer & Industry UsageTech companies, startups, research labsTech, finance, healthcare, consulting firms

New Grad Machine Learning roles typically focus on foundational skills, internships, and entry-level tasks, while Data Scientist positions often require more experience in data analysis and statistical modeling. Both roles are common in tech industries, but Data Scientists usually handle broader data analysis responsibilities.

What are 'New Grad Machine Learning' roles?

New Grad Machine Learning roles are entry-level positions designed for recent graduates who have studied machine learning, artificial intelligence, data science, or related fields. These positions typically involve working with experienced data scientists and engineers to develop, implement, and improve machine learning models and algorithms. New grads in these roles often contribute to projects involving data preprocessing, model training, evaluation, and deployment. The goal is to help new graduates gain hands-on experience and grow their skills in a real-world setting while contributing to the organization's AI initiatives.
What cities near Haslet, TX are hiring for New Grad Machine Learning jobs? Cities near Haslet, TX with the most New Grad Machine Learning job openings:
New Grad Machine Learning jobs near you
Senior Applied Scientist, Machine Learning

Senior Applied Scientist, Machine Learning

Skyhigh Networks

Frisco, TX • Hybrid

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 10 days ago

Job description

Role Overview:We are seeking a Senior Applied Scientist to join McAfee's Consumer ML team and drive AI-powered solutions that deliver personalized experiences, optimize pricing, and improve payment success for millions of global customers. In this role, you will lead the end-to-end development and deployment of high-impact ML models across fraud detection, dynamic pricing, journey optimization, and contextual recommendation systems. You'll design and execute experimentation frameworks, champion GenAI tooling adoption to accelerate development, and apply advanced techniques, including deep learning and reinforcement learning. This is a hands-on technical leadership role requiring 8+ years of Applied ML experience, proven expertise in personalization, pricing optimization, or churn/propensity modeling for digital subscriptions, and strong cross-functional collaboration skills to translate ML innovation into measurable business outcomes.
This is a Hybrid position located in Frisco, TX. You will be required to be on-site on an as-needed basis; when you are not working on-site, you will work from your home office. You must be within commutable distance of Frisco, TX. We are not offering relocation assistance at this time.

About the Role

  • Strategic Vision: Drive the ML science strategy for pricing, recommendation systems, and personalized consumer experiences, to maximize McAfee's customer value.

  • Model Development: Lead the research, implementation, and delivery of Applied AI/ML models using user behavior and subscription data to enhance personalization and product value.

  • Optimization & Experimentation: Lead algorithm development to optimize consumer journeys, increase conversion rates, and drive monetization strategies. Design and execute controlled experiments (A/B and multivariate tests) to validate and enhance model performance.

  • Generative AI Enablement: Leverage GenAI tools-such as GitHub Copilot, Claude Code, and other AI coding assistants-to amplify development productivity in data preparation, model tuning, and orchestration workflows. Champion the integration of GenAI capabilities into the ML lifecycle to accelerate experimentation and reduce time-to-market.

  • Research & Knowledge Sharing: Stay at the forefront of ML science, contributing to the development of new algorithms and applications. Share knowledge through internal presentations, publications, and participation in academic or industry forums.

  • Reinforcement Learning is a Plus: Guide the team in applying reinforcement learning methods such as contextual bandits, SARSA, and Q-learning. Implement exploration-exploitation strategies, including epsilon-greedy, Thompson sampling, and Upper Confidence Bound (UCB) to optimize decision-making for pricing and recommendation engines.

  • Cross-Functional Collaboration: Partner with Marketing, Product, Sales, and Engineering teams to ensure ML solutions align with strategic objectives and deliver measurable business impact.

About You

  • Experience: 8+ years of expertise in Applied AI & ML, complemented by at least 3 years of technical leadership experience mentoring machine learning scientists in technical capacities.

  • Mandatory Qualification: Proven track record in at least one of the following: implementing AI/ML-based personalized messaging techniques to enhance consumer/customer product experiences; developing AI/ML-based dynamic pricing and personalized offer strategies for pricing optimization; or creating customer/consumer churn and propensity models specifically for digital subscription use cases

  • Technical Expertise: Deep proficiency in classical ML and deep learning techniques (e.g., XGBoost, Random Forest, SVMs, deep neural networks), autoencoders, representation learning, and deep recommender system techniques, as well as reinforcement learning methods (contextual bandits, SARSA, Q-learning). Strong programming skills in Python, SQL, and ML frameworks.

  • Tooling & Libraries: Proficient with ML libraries such as PyTorch and Scikit-learn, with a strong background in feature engineering, model validation, and evaluation metrics.

  • Mathematical Foundations: Solid understanding of the mathematical and statistical principles underpinning ML algorithms (linear algebra, calculus, probability) and a passion for solving complex problems through research and application of emerging techniques.

  • Communication & Collaboration: Excellent communicator who can distill complex ML concepts for both technical and non-technical stakeholders and collaborate effectively across cross-functional teams to align ML models with business goals.

#LI-Hybrid


Company Overview

McAfee is a leader in personal security for consumers. Focused on protecting people, not just devices, McAfee consumer solutions adapt to users' needs in an always online world, empowering them to live securely through integrated, intuitive solutions that protects their families and communities with the right security at the right moment.

Company Benefits and Perks:

We work hard to embrace diversity and inclusion and encourage everyone at McAfee to bring their authentic selves to work every day. We're proud to be Great Place to Work Certified in 10 countries, a reflection of the supportive, empowering environment we've built where people feel seen, valued, and energized to reach their full potential and thrive.

We offer a variety of social programs, flexible work hours and family-friendly benefits to all of our employees.

  • Bonus Program
  • Pension and Retirement Plans
  • Medical, Dental and Vision Coverage
  • Paid Time Off
  • Paid Parental Leave
  • Support for Community Involvement

We're serious about our commitment to diversity which is why McAfee prohibits discrimination based on race, color, religion, gender, national origin, age, disability, veteran status, marital status, pregnancy, gender expression or identity, sexual orientation or any other legally protected status.

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