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Machine Learning Biomedical Engineer Jobs in Flower Mound, TX

UNAVAILABLE

Machine Learning Engineer

Frisco, TX · On-site

Overview Quarterhill is seeking a Machine Learning Engineer to join our forward-thinking team building the next generation of Intelligent Transportation Systems . In this role, you will design ...

Avita Care Solutions

Machine Learning Engineer

Plano, TX · Remote

$140K - $185K/yr

The Machine Learning Engineer will have deep expertise in healthcare and pharmacy data. The focus of the role is on designing scalable, reliable, and compliant ML solutions that support improved ...

New

Quarterhill

Machine Learning Engineer

Frisco, TX · On-site

Overview Quarterhill is seeking a Machine Learning Engineer to join our forward-thinking team building the next generation of Intelligent Transportation Systems . In this role, you will design ...

7-ELEVEN

Machine Learning Engineer II

Irving, TX · On-site

As a Machine Learning Engineer II at 7-Eleven, you will collaborate with cross-functional teams and work closely with senior architects to implement scalable and efficient data solutions. Your ...

7-ELEVEN

Machine Learning Engineer II

Irving, TX · On-site

As a Machine Learning Engineer II at 7-Eleven, you will collaborate with cross-functional teams and work closely with senior architects to implement scalable and efficient data solutions. Your ...

7-Eleven, Inc

Machine Learning Engineer II

Irving, TX · On-site

As a Machine Learning Engineer II at 7-Eleven, you will collaborate with cross-functional teams and work closely with senior architects to implement scalable and efficient data solutions. Your ...

7-Eleven

Machine Learning Engineer II

Irving, TX · On-site

As a Machine Learning Engineer II at 7-Eleven, you will collaborate with cross-functional teams and work closely with senior architects to implement scalable and efficient data solutions. Your ...

7-Eleven

Machine Learning Engineer II

Irving, TX · On-site

As a Machine Learning Engineer II at 7-Eleven, you will collaborate with cross-functional teams and work closely with senior architects to implement scalable and efficient data solutions. Your ...

Yum! Brands

Machine Learning Engineer II

Plano, TX · On-site

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 ...

Capital One

Lead Machine Learning Engineer

Plano, TX

$98K - $129K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You ...

TeleWorld Solutions Inc

Machine Learning Engineer

Plano, TX · On-site

$53.50 - $71/hr

As a Performance Assurance Machine Learning Engineer, you will work under the coaching of Senior and Lead engineers of the Data Science & Tools Team to analyze Samsung's deployed network elements.

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All JobsMachine Learning Biomedical Engineer JobsMachine Learning Biomedical Engineer Jobs in Flower Mound, TX

Machine Learning Biomedical Engineer information

See Flower Mound, TX salary details

$28.9K

$118.1K

$177.5K

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

As of Sep 14, 2026, the average yearly pay for machine learning biomedical engineer in Flower Mound, TX is $118,114.00, according to ZipRecruiter salary data. Most workers in this role earn between $93,100.00 and $142,200.00 per year, depending on experience, location, and employer.

What does a machine learning biomedical engineer do?

A Machine Learning Biomedical Engineer applies machine learning techniques to solve problems in biology and medicine. They develop algorithms and models to analyze complex biomedical data, such as medical images, genetic information, or sensor readings. Their work supports advancements in diagnostics, treatment planning, and personalized medicine. Typically, they collaborate with clinicians, researchers, and other engineers to design systems that improve healthcare outcomes.

How does a machine learning biomedical engineer typically collaborate with clinicians and researchers in a healthcare setting?

Machine Learning Biomedical Engineers often work closely with clinicians and researchers to develop algorithms that solve real-world medical challenges. Collaboration usually involves understanding clinical needs, translating them into technical requirements, and iteratively refining models based on feedback from medical experts. Regular meetings, interdisciplinary project teams, and direct participation in data collection or validation studies are common. This collaborative environment ensures that technical solutions are both innovative and clinically relevant, making communication and adaptability essential skills.

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

To thrive as a Machine Learning Biomedical Engineer, you need a strong background in biomedical engineering, data analysis, and machine learning, typically supported by a degree in biomedical engineering, computer science, or a related field. Familiarity with programming languages like Python or R, machine learning frameworks (e.g., TensorFlow, PyTorch), and experience with medical imaging or signal processing tools are commonly required. Critical thinking, problem-solving, and the ability to communicate complex technical concepts to interdisciplinary teams are vital soft skills. These abilities are crucial for developing innovative healthcare solutions, ensuring regulatory compliance, and bridging the gap between technology and medicine.

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

AspectMachine Learning Biomedical EngineerData Scientist in Biomedical Industry
Required CredentialsDegree in Biomedical Engineering, Computer Science, or related fields; knowledge of machine learning and biomedical dataDegree in Data Science, Statistics, or related fields; proficiency in data analysis and machine learning
Work EnvironmentResearch labs, healthcare institutions, biotech companiesHealthcare analytics firms, research institutions, biotech companies
Employer & Industry UsageDevelops algorithms for medical devices, diagnostics, and treatment planningAnalyzes biomedical data to inform clinical decisions, research, and product development

Both roles require expertise in machine learning and biomedical data, but Machine Learning Biomedical Engineers focus on developing algorithms for medical applications, while Data Scientists analyze biomedical data to support research and clinical decisions.

What are popular job titles related to Machine Learning Biomedical Engineer jobs in Flower Mound, TX?

For Machine Learning Biomedical Engineer jobs in Flower Mound, TX, the most frequently searched job titles are:

What cities near Flower Mound, TX are hiring for Machine Learning Biomedical Engineer jobs?

Cities near Flower Mound, TX with the most Machine Learning Biomedical Engineer job openings:

Machine Learning Biomedical Engineer jobs near you
Infographic showing various Machine Learning Biomedical Engineer job openings in Flower Mound, TX as of September 2026, with employment types broken down into 8% Internship, 74% Full Time, and 18% Part Time. Highlights an 89% In-person, and 11% Hybrid job distribution, with an average salary of $118,114 per year, or $56.8 per hour.

Machine Learning Engineer

Frisco, TX • On-site

UNAVAILABLE
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Re-posted 9 days ago

Job description

Overview

Quarterhill is seeking a Machine Learning Engineer to join our forward-thinking team building the next generation of Intelligent Transportation Systems. In this role, you will design, develop, and deploy state-of-the-art computer vision and language models that power scalable, real-world solutions. You’ll work with large-scale image and video data, building and optimizing production-grade vision systems while contributing clean and modular code to shared repositories.

As part of our AI team, you’ll collaborate closely with engineering teams to deliver high-impact features for our growing SaaS platform. The ideal candidate brings hands‑on experience deploying computer vision and language models in production and applying MLOps best practices on cloud platforms.

Responsibilities
  • Fine‑tune and deploy computer vision and deep learning models for object detection, object tracking, and OCR at scale.
  • Develop vision‑language models and Mixture of Experts architectures, from experimental design through production deployment.
  • Architect Retrieval‑Augmented Generation (RAG) systems, including vector store design, hybrid search strategies, chunking pipelines, and context relevance evaluation.
  • Apply MLOps best practices for training, evaluation, deployment, and monitoring of production grade computer vision models, with an emphasis on clean, modular, maintainable code.
  • Contribute to our machine learning repositories and optimize models for performance, scalability, and real‑time inference across edge and cloud environments.
  • Drive performance optimization and scalability of ML systems across edge and cloud environments.
  • Collaborate with cross‑functional teams to integrate computer vision solutions into end‑to‑end products, translating research outcomes into measurable platform impact.

This list of responsibilities might not cover everything you'll end up doing.

Qualifications
  • 5+ years of hands‑on machine learning experience, with deep specialization in computer vision and a proven track record of shipping models to production.
  • Master's degree required (Ph.D. preferred) in Computer Science, Machine Learning, or a closely related field.
  • Extensive knowledge of computer vision architectures such as Vision Transformers and VLMs along with OpenCV and PIL.
  • Experience with MLOps tools (MLflow, Kubeflow, Docker, Kubernetes) able to own the full model lifecycle from experimentation through production monitoring.
  • Experience building and deploying LLM-based systems and Retrieval‑Augmented Generation (RAG) pipelines, including vector store integration and retrieval evaluation.
  • Strong communicator who can translate complex research findings into actionable decisions for engineering and product stakeholders.
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