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Remote Full Stack Machine Learning Engineer Jobs in Texas

Shipwell

Machine Learning Engineer

Austin, TX · On-site +1

Your work will span the full machine learning lifecycle--from extracting and transforming data from ... Working closely with engineering, analytics, data science, and product teams, you'll take our ...

PTP

Machine Learning Engineer - REMOTE

Austin, TX · On-site +1

We are looking for a Machine Learning Engineer to help us design and deliver CX solutions that provide our clients with a beautiful customer journey that achieves results. At PTP we value aptitude ...

Capital One

Senior Machine Learning Engineer

Plano, TX · On-site +1

$100K - $137.30K/yr

Senior Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part ... Eligibility varies based on full or part-time status, exempt or non-exempt status, and management ...

Capital One

Lead Machine Learning Engineer

Plano, TX · On-site +1

$98.10K - $129.20K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of ... Eligibility varies based on full or part-time status, exempt or non-exempt status, and management ...

Striveworks

Senior Machine Learning Engineer

Austin, TX · On-site +1

$121.40K - $160.10K/yr

Opportunities to conduct mission-critical field work This position offers a fully remote work ... Experience with a variety of unstructured data types (e.g., imagery, full motion video, text ...

Striveworks

Staff Machine Learning Engineer

Austin, TX · On-site +1

This position offers a fully remote work environment, or you can work hybrid/on site at our office ... Experience with complex and varied data types, such as multi-spectral imagery, full motion video ...

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Remote Full Stack Machine Learning Engineer information

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

To thrive as a Remote Full Stack Machine Learning Engineer, you need proficiency in programming languages (such as Python or JavaScript), a solid understanding of machine learning algorithms, experience with web development frameworks, and typically a degree in computer science or a related field. Familiarity with tools like TensorFlow, PyTorch, Docker, cloud computing platforms (AWS, GCP), and version control systems (Git) is essential. Strong problem-solving skills, self-motivation, and clear communication are crucial soft skills, especially in remote and cross-functional team environments. These combined skills ensure effective design, deployment, and integration of machine learning solutions in scalable web applications while maintaining productivity in a remote setting.

What are some common challenges faced by remote Full Stack Machine Learning Engineers, and how can they be addressed?

Remote Full Stack Machine Learning Engineers often encounter challenges such as managing effective collaboration with cross-functional teams and ensuring smooth deployment of machine learning models into production environments. To address these, it's important to establish clear communication channels, regularly participate in virtual stand-ups, and use collaborative platforms such as GitHub and Slack. Additionally, staying organized with version control and thorough documentation helps maintain project transparency and ensures seamless handoffs between backend and frontend development. Proactively seeking feedback and scheduling regular check-ins with team members can further enhance productivity and integration within the team.

What is a Remote Full Stack Machine Learning Engineer?

A Remote Full Stack Machine Learning Engineer is a professional who designs, develops, and deploys machine learning solutions while working remotely. They handle both the front-end and back-end aspects of machine learning projects, including data preprocessing, model building, API development, and integration with user interfaces or cloud platforms. This role requires expertise in programming, machine learning frameworks, cloud services, and web technologies, allowing them to build end-to-end AI-driven applications from anywhere in the world.

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

AspectRemote Full Stack Machine Learning EngineerRemote Data Scientist
Primary FocusDeveloping end-to-end machine learning applications, including backend, frontend, and model deploymentAnalyzing data, creating models, and generating insights without necessarily building full applications
Skills RequiredProgramming (Python, JavaScript), ML frameworks, web development, deployment toolsStatistics, data analysis, visualization, Python/R, SQL
Work EnvironmentCollaborates with developers, data engineers, and product teams in tech-driven companiesWorks with data teams, analysts, and business units in various industries

While both roles involve working with data and machine learning, a Remote Full Stack Machine Learning Engineer builds complete applications with integrated ML models, whereas a Remote Data Scientist focuses on data analysis and model creation without necessarily developing full applications.

What are the most commonly searched types of Full Stack Machine Learning Engineer jobs in Texas? The most popular types of Full Stack Machine Learning Engineer jobs in Texas are:
What are popular job titles related to Remote Full Stack Machine Learning Engineer jobs in Texas? For Remote Full Stack Machine Learning Engineer jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Remote Full Stack Machine Learning Engineer jobs in Texas look for? The top searched job categories for Remote Full Stack Machine Learning Engineer jobs in Texas are:
What cities in Texas are hiring for Remote Full Stack Machine Learning Engineer jobs? Cities in Texas with the most Remote Full Stack Machine Learning Engineer job openings:
Remote Full Stack 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 27 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 184 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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