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Machine Learning Engineer Jobs in Frisco, TX (NOW HIRING)

AppFolio

Sr. Machine Learning Engineer

Richardson, TX · Remote

$94.30K - $129.50K/yr

Who we are looking for We're seeking a Sr Machine Learning Engineer to play a critical role in shaping Realm-X and the future of AI at AppFolio. This is a high-impact position focused on defining ...

AppFolio

Sr. Machine Learning Engineer

Richardson, TX · Remote

$94.30K - $129.50K/yr

Who we are looking for We're seeking a Sr Machine Learning Engineer to play a critical role in shaping Realm-X and the future of AI at AppFolio. This is a high-impact position focused on defining ...

Cisco

Principal Machine Learning Engineer

Dallas, TX · On-site

$291.50K - $369.10K/yr

Partner with executive leadership, engineering, product, and data science teams to ensure AI ... Solid proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow) * Experience ...

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Resolve Tech Solutions

Senior ML Engineer

Addison, TX

$101.20K - $138.90K/yr

Develop machine learning models and algorithms to address business needs. Collaborate with data scientists and software engineers to design and implement scalable and efficient solutions. Clean ...

Resolve Tech Solutions

Senior ML Engineer

Addison, TX · On-site

$101.20K - $138.90K/yr

Responsibilities: • Develop machine learning models and algorithms to address business needs. • Collaborate with data scientists and software engineers to design and implement scalable and ...

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Machine Learning Engineer information

See Frisco, TX salary details

$29.5K

$120.5K

$181.1K

How much do machine learning engineer jobs pay per year?

As of May 30, 2026, the average yearly pay for machine learning engineer in Frisco, TX is $120,520.00, according to ZipRecruiter salary data. Most workers in this role earn between $95,000.00 and $145,100.00 per year, depending on experience, location, and employer.

What Does a Machine Learning Engineer Do?

A machine learning engineer maintains production systems and often works with other engineers. In this career, you work with software development methodology, use modern software development tools, and use agile practices. You also play a role in software design and architecture, so you may occasionally work with a programmer. An engineer may help to predict how a model should perform or seek out regression issues by using different test types and algorithms. To fulfill your duties and responsibilities, you work on a computer and use an array of skills and programs to carry out these tests.

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

To thrive as a Machine Learning Engineer, you need strong programming skills (particularly in Python), a solid background in mathematics and statistics, and a degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and cloud platforms is typically required. Problem-solving ability, effective communication, and adaptability are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies ensure the development, deployment, and continual improvement of machine learning systems that drive business value.

What are some common challenges faced by Machine Learning Engineers when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining data consistency between training and production environments, and monitoring model performance over time. Integrating models into existing software infrastructure may require collaboration with DevOps and software engineering teams to address issues like latency, version control, and resource allocation. Additionally, ongoing model maintenance is crucial to prevent model drift and ensure that predictions remain accurate as new data becomes available.

What are Machine Learning Engineers?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready solutions. Their responsibilities include data preprocessing, model selection, algorithm implementation, and optimizing models for performance and efficiency. Machine Learning Engineers often collaborate with data scientists, software developers, and other stakeholders to integrate AI technologies into products and services.

What jobs make $3,000 a month without a degree?

A Machine Learning Engineer typically requires a degree, but roles such as data annotator, technical support specialist, or freelance programmer can sometimes earn around $3,000 monthly without a formal degree, especially with relevant skills and experience. These jobs often involve self-taught skills, online certifications, or on-the-job training and may require proficiency in tools like Python or cloud platforms.

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

AspectMachine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops scalable ML models, deploys algorithms into productionAnalyzes data, builds models, interprets data insights
Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research organizations

While both roles work with data and machine learning, Machine Learning Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights. The roles often overlap but differ in their core responsibilities and focus areas.

More about Machine Learning Engineer jobs
What are the most commonly searched types of Machine Learning Engineer jobs in Frisco, TX? The most popular types of Machine Learning Engineer jobs in Frisco, TX are:
What are popular job titles related to Machine Learning Engineer jobs in Frisco, TX? For Machine Learning Engineer jobs in Frisco, TX, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer jobs in Frisco, TX look for? The top searched job categories for Machine Learning Engineer jobs in Frisco, TX are:
What cities near Frisco, TX are hiring for Machine Learning Engineer jobs? Cities near Frisco, TX with the most Machine Learning Engineer job openings:
Machine Learning Engineer jobs near you
Machine Learning Software Engineer II

Machine Learning Software Engineer II

Cambium Learning Group

Dallas, TX • On-site, Remote

$89.90K - $123.10K/yr

Full-time

Posted 25 days ago

Cambium Learning Group rating

9.2

Company rating: 9.2 out of 10

Based on 5 frontline employees who took The Breakroom Quiz

12th of 183 rated software companies

Job description

Cambium Learning® Group is an award-winning educational technology solutions leader dedicated to helping all students reach their potential through individualized and differentiated instruction. Using a research-based, personalized approach, Cambium Learning Group delivers SaaS resources and instructional products that engage students and support teachers in fun, positive, safe and scalable environments. These solutions are provided through Learning A-Z® (online differentiated instruction for elementary school reading, writing and science), ExploreLearning® (online interactive math and science simulations, a math fact fluency solution, and a K-2 science solution), Voyager Sopris Learning® (blended solutions that accelerate struggling learners to achieve in literacy and math and professional development for teachers), and VKidz Learning (online comprehensive homeschool education and programs for literacy and science). We believe that every student has unlimited potential, that teachers matter, and that data, instruction, and practice are the keys to success in the classroom and beyond.
Job Overview:
We are seeking a talented Machine Learning Engineer II to join our CAI machine learning and scoring development team. In this role, you will be the crucial bridge between applied research and production systems. Working alongside a cross-functional group of mathematicians, computer scientists, psychometricians, and statisticians, you will design and deploy custom machine learning solutions for our clients and internal platforms.
The ideal candidate is a full-stack ML practitioner who is equally comfortable discussing algorithmic design with researchers and architecting scalable, low-latency production systems. You will own the full software development lifecycle-transforming research prototypes into optimized, production-ready solutions using modern AWS infrastructure such as SageMaker, ECS, and Lambda, with an emphasis on high-throughput inference and PyTorch-to-ONNX model optimization.
Job Responsibilities:
  • Full-Lifecycle ML Development: Lead the transition of machine learning models from theoretical prototypes into scalable, high-performance production systems.
  • AWS Cloud Architecture & Deployment: Architect and deploy ML solutions utilizing AWS ECS (Elastic Container Service) for containerized workloads and AWS Lambda for serverless, event-driven inference pipelines.
  • Model & Inference Optimization: Optimize PyTorch models for production deployment by converting them to ONNX formats. Apply advanced inference optimization techniques (quantization, pruning, ONNX Runtime) and memory-efficient attention mechanisms like Flash Attention to minimize latency and maximize throughput.
  • Infrastructure & Engineering Best Practices: Champion infrastructure best practices for machine learning systems, establishing reliable CI/CD pipelines, and ensuring robust, secure, and reproducible deployments across the AWS ecosystem.
  • Algorithm Engineering: Design, develop, and evaluate algorithms that generate descriptive, diagnostic, predictive, and prescriptive insights from both structured and unstructured data.
  • Robust Software Engineering: Write clean, efficient, and well-tested code. Complete rigorous testing, debugging, and documentation to ensure seamless installation and long-term maintenance.
  • Cross-Functional Collaboration: Actively participate in research discussions, requirements gathering, and system design alongside domain experts to build tailored scoring and ML solutions.

Job Requirements:
  • Experience: 2-5 years of industry experience in Machine Learning Engineering, Software Engineering, or Data Science, with a proven track record of architecting and deploying models to production.
  • Cloud & MLOps Infrastructure: Deep, hands-on experience with the AWS ecosystem, specifically AWS ECS and Lambda. Solid understanding of containerization (Docker) and event-driven architectures.
  • Programming Proficiency: Strong proficiency in modern programming languages used in ML (e.g., Python, C++, Java) and familiarity with industry-standard coding practices.
  • ML Frameworks & Advanced Optimization: Hands-on experience with PyTorch and other machine learning libraries (e.g., Scikit-Learn, TensorFlow). Deep understanding of model optimization pipelines, including PyTorch to ONNX conversions, ONNX Runtime, and scaling attention mechanisms (e.g., Flash Attention).
  • Data Systems: Experience working with large-scale computing frameworks, data analysis systems, and relational/non-relational databases.

Nice to Have's:
  • AWS SageMaker: Experience utilizing AWS SageMaker for managed model training and hosting.
  • Advanced LLMOps & Fine-Tuning: Hands-on experience applying modern parameter-efficient fine-tuning methods (such as LoRA and qLoRA) to large language models.
  • AI Agents: Experience building, integrating, and deploying autonomous or semi-autonomous AI agents to automate complex workflows and connect ML models with external tools/APIs.
  • NLP Expertise: Proven experience and familiarity with deep learning technologies applied specifically to Natural Language Processing (NLP) and complex text-based modeling.
  • Cross-Disciplinary Collaboration: Experience collaborating with specialized researchers (e.g., psychometricians, statisticians) to operationalize complex mathematical concepts.
  • Infrastructure as Code: Experience implementing IaC using tools like Terraform or AWS CloudFormation.
  • Model Monitoring: Experience setting up comprehensive model monitoring systems to detect data drift, concept drift, and model degradation in production AWS environments.

To apply for this opportunity, simply click on the "Apply" button and submit a cover letter and resume.
An Equal Opportunity Employer
We are dedicated to fostering a culture that celebrates unique backgrounds, ideas, and experiences. All qualified applicants will receive consideration for employment without discrimination on the basis of race, color, religion, sex, gender, gender identity/expression, sexual orientation, national origin, protected veteran status, or disability.

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