1

Machine Learning Biomedical Engineer Jobs in Texas

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 ...

Engineers on the BCI team utilize signal processing and machine learning to communicate with the brain. You will have access to the most cutting-edge neural interface hardware and develop ...

Machine Learning Engineer LOCATION San Antonio, TX 78208 CLEARANCE TS/SCI Full Poly (Please note this position requires full U.S. Citizenship) KEY SUMMARY We are seeking a talented and innovative ...

Machine Learning Engineer

Austin, TX ยท On-site

$199K - $331K/yr

Engineers on the BCI team utilize signal processing and machine learning to communicate with the brain. You will have access to the most cutting-edge neural interface hardware and develop ...

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 ...

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 ...

Senior 5G RAN Machine Learning Engineer Duration: 6 Months (Contract) Employment Type: W2 Only Pay Rate: Up to $70/hr on W2 Job Summary: We are seeking a Senior 5G RAN Machine Learning Engineer to ...

next page

Showing results 1-20

Machine Learning Biomedical Engineer information

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 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.

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.

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 job categories do people searching Machine Learning Biomedical Engineer jobs in Texas look for? The top searched job categories for Machine Learning Biomedical Engineer jobs in Texas are:
What cities in Texas are hiring for Machine Learning Biomedical Engineer jobs? Cities in Texas with the most Machine Learning Biomedical Engineer job openings:
Infographic showing various Machine Learning Biomedical Engineer job openings in Texas as of July 2026, with employment types broken down into 1% As Needed, 84% Full Time, 14% Part Time, and 1% Contract. Highlights an 86% Physical, 3% Hybrid, and 11% Remote job distribution.

Machine Learning Engineer

Lorvin Technologies

Fort Worth, TX โ€ข On-site

Other

This job post hasย expired today.ย Applications are no longer accepted.


Job description

Job Description -

  • Collaborate with leaders, business analysts, project managers, IT architects, technical leads and other engineers, along with internal customers, to understand requirements and develop needs according to business requirements for AI solutions
  • Maintain and enhance existing enterprise services, applications, and platforms using domain driven design and test-driven development
  • Troubleshoot and debug complex issues; identify and implement solutions
  • Create detailed project specifications, requirements, and estimates
  • Research and implement new AI technologies to enhance current processes, security, and performance
  • Work closely with data scientists and product teams to build and deploy machine learning models, focusing on the technical aspects of model deployment.
  • Implement and optimize Python-based ML pipelines for data preprocessing, model training, and deployment.
  • Monitor model performance and implement strategies for bias mitigation and explainability. Responsible for ensuring models are scalable and efficient in production environments.
  • Write and maintain code for model training and deployment, collaborating with software engineers to integrate models into applications.
  • Partner with a diverse team of experts, leveraging cutting-edge technologies to build scalable and impactful AI solutions.

Minimum Qualifications Education & Prior Job Experience

  • Bachelor's degree in Computer Science, Computer Engineering, Data Science, Information Systems (CIS/MIS), Engineering or related technical discipline, or equivalent experience/training
  • 7 to 9+ years of full Software Development Life Cycle (SDLC) experience designing, developing, and implementing large-scale machine learning applications in hosted production environments
  • 2+ years of professional, design, and open-source experience