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Math Software Engineer Jobs in Garland, TX (NOW HIRING)

L3HHCM20

Associate, Software Engineering

Greenville, TX · On-site

Greenville, TX * Apply computer science, engineering, and mathematical analysis concepts and principles in the development of software for the target application * Work closely with cross functional ...

Echo IT Solutions

Sr Software Engineer AI-ML

Irving, TX · On-site

$113K - $149K/yr

Echo IT Solutions is a company seeking a Senior Software Engineer specializing in AI and Machine ... mathematics • High proficiency in Python is mandatory • R, C++, or Java • Expertise in ...

TekWissen LLC

Software Dev Engineer IV

Dallas, TX · On-site

MS in Computer Science, Computer or Electrical Engineering, Mathematics, or a related field plus one year of experience in the job offered or related occupations of Software Engineer, Software ...

Socket.dev

Senior Test Software Engineer

Mckinney, TX · On-site

$87 - $165/hr

Typically requires a degree in Science, Technology, Engineering or Mathematics (STEM) and a minimum ... Experience developing and integrating software applications using C, C++, C#, NI LabVIEW, NI ...

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All JobsMath Software Engineer JobsMath Software Engineer Jobs in Garland, TX

Math Software Engineer information

See Garland, TX salary details

$61.4K

$142.5K

$198.5K

How much do math software engineer jobs pay per year?

As of Aug 12, 2026, the average yearly pay for math software engineer in Garland, TX is $142,534.00, according to ZipRecruiter salary data. Most workers in this role earn between $115,900.00 and $167,100.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a math software engineer, and why are they important?

To thrive as a Math Software Engineer, you need a strong background in mathematics, computer science, and algorithm development, typically supported by a relevant degree. Proficiency in programming languages such as Python, C++, or MATLAB, along with experience using mathematical libraries and tools like NumPy or SciPy, is essential. Analytical thinking, problem-solving, and effective collaboration are valuable soft skills that enhance performance in this role. These skills ensure the development of robust, efficient, and accurate mathematical software solutions that meet complex computational requirements.

How do math software engineers typically collaborate with other teams during the development process?

Math Software Engineers often work closely with cross-functional teams, such as data scientists, product managers, and front-end developers, to ensure mathematical models and algorithms are accurately implemented in software products. Collaboration involves regular meetings to discuss requirements, problem-solving sessions to address computational challenges, and code reviews for maintaining mathematical integrity. Communicating complex mathematical concepts in an accessible way is a key part of the role, enabling teams to create robust and efficient solutions that meet user needs.

What is a math software engineer?

Math Software Engineers are professionals who design, develop, and optimize software that performs complex mathematical computations. They often work on algorithms, numerical analysis, and simulation tools used in scientific research, finance, engineering, or data analysis. Their work ensures that mathematical models and computations are both accurate and efficient within various applications. Math Software Engineers typically have a strong background in mathematics, computer science, and programming languages such as Python, C++, or MATLAB.

What is the difference between Math Software Engineer vs Data Scientist?

AspectMath Software EngineerData Scientist
Required CredentialsBachelor's or higher in Computer Science, Mathematics, or related fieldsBachelor's or higher in Statistics, Data Science, or related fields
Work EnvironmentSoftware development teams, R&D labs, tech companiesData analysis teams, research departments, tech firms
Industry UsageDeveloping algorithms, modeling, simulationData analysis, predictive modeling, insights generation

Math Software Engineers focus on developing mathematical algorithms and software solutions, often working on simulations and modeling. Data Scientists analyze data to extract insights and build predictive models. While both roles require strong math skills, Math Software Engineers are more involved in software development, whereas Data Scientists focus on data analysis and interpretation.

What are popular job titles related to Math Software Engineer jobs in Garland, TX? For Math Software Engineer jobs in Garland, TX, the most frequently searched job titles are:
What job categories do people searching Math Software Engineer jobs in Garland, TX look for? The top searched job categories for Math Software Engineer jobs in Garland, TX are:
What cities near Garland, TX are hiring for Math Software Engineer jobs? Cities near Garland, TX with the most Math Software Engineer job openings:
Math Software Engineer jobs near you

Machine Learning Software Engineer II

Cambium Learning Group

Dallas, TX • On-site, Remote

$89K - $123K/yr

Full-time

Re-posted 9 days ago

Cambium Learning Group rating

9.5

Company rating: 9.5 out of 10

Based on 6 frontline employees who took The Breakroom Quiz

10th of 243 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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