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Machine Learning Engineer Biotech Jobs in Shepherd, TX

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

AI Lead 11+ years of exp

Houston, TX

$133K - $164K/yr

Proven experience as an AI Engineer, Machine Learning Engineer, or similar role, with a portfolio of delivered AI/Gen AI solutions. * Proficiency in AI platforms and tools such as Azure OpenAI ...

PMP, CSM, or AI certifications (e.g., Google Professional Machine Learning Engineer) preferred. * 5+ years in program/project management, with 3+ years focused on AI/ML, cloud platforms (AWS, Azure ...

AI Solutions Architect

Houston, TX

$60.25 - $79.25/hr

Certifications in artificial intelligence, machine learning, or cloud platforms, such as AWS Certified Machine Learning - Specialty, Google Cloud Professional Machine Learning Engineer, Microsoft ...

Work You'll Do As a Senior AI Engineer, you'll work cross-functionally with data scientists, machine learning engineers, project managers, and industry experts to develop robust AI infrastructure and ...

Software Engineer in Data Science

Houston, TX ยท On-site

$109K - $131K/yr

The individual will work both with our data scientists and machine learning engineers but will also need to directly engage with the commercial teams (across trading, operations, support functions ...

Software Engineer in Data Science

Houston, TX ยท On-site +1

$109K - $131K/yr

The individual will work both with our data scientists and machine learning engineers but will also need to directly engage with the commercial teams (across trading, operations, support functions ...

Software Engineer in Data Science

Houston, TX ยท On-site +1

$109K - $131K/yr

The individual will work both with our data scientists and machine learning engineers but will also need to directly engage with the commercial teams (across trading, operations, support functions ...

Software Engineer in Data Science

Houston, TX ยท On-site +1

$109K - $131K/yr

The individual will work both with our data scientists and machine learning engineers but will also need to directly engage with the commercial teams (across trading, operations, support functions ...

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Showing results 1-20

Machine Learning Engineer Biotech information

See Shepherd, TX salary details

$27.7K

$113.3K

$170.3K

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

As of Jun 13, 2026, the average yearly pay for machine learning engineer biotech in Shepherd, TX is $113,324.00, according to ZipRecruiter salary data. Most workers in this role earn between $89,300.00 and $136,400.00 per year, depending on experience, location, and employer.

What does a Machine Learning Engineer do in the biotech industry?

A Machine Learning Engineer in biotech applies advanced algorithms and data analysis techniques to solve biological and medical problems. They work with large datasets such as genomic sequences, medical images, or clinical records to develop predictive models, automate data analysis, and uncover insights that can accelerate drug discovery, diagnostics, and personalized medicine. Their work often involves close collaboration with biologists, data scientists, and software engineers to create tools and solutions that improve healthcare outcomes. Machine Learning Engineers in this field need a strong background in both computational methods and biological sciences.

How do Machine Learning Engineers in biotech typically collaborate with research scientists and domain experts?

Machine Learning Engineers in biotech often work closely with research scientists and domain experts to translate complex biological problems into data-driven solutions. This collaboration involves regular meetings to understand experimental data, refine project goals, and iterate on model development based on domain feedback. Engineers are expected to communicate technical concepts clearly, adapt models to fit scientific needs, and help validate results alongside laboratory teams. This interdisciplinary environment fosters innovation but also requires flexibility and strong communication skills.

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

To thrive as a Machine Learning Engineer in Biotech, you need a solid background in computer science, statistics, and biology, often with an advanced degree in a related field. Experience with programming languages such as Python or R, machine learning frameworks like TensorFlow or PyTorch, and familiarity with bioinformatics tools are typically required. Strong problem-solving, communication, and interdisciplinary collaboration skills set standout candidates apart. These capabilities are crucial for developing effective models that drive scientific innovation and advance biotechnological research.

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

AspectMachine Learning Engineer BiotechData Scientist Biotech
Required CredentialsBachelor's or Master's in Computer Science, Data Science, or related; knowledge of ML frameworksBachelor's or Master's in Data Science, Statistics, or related; strong analytical skills
Work EnvironmentDevelops ML models, coding, deploying algorithms in biotech R&DAnalyzes biological data, interprets results, creates reports
Employer & Industry UsageBiotech firms, pharma companies, research labsBiotech companies, healthcare, research institutions

While both roles work with biological data, Machine Learning Engineers focus on developing and deploying ML algorithms, whereas Data Scientists analyze and interpret biological datasets to inform research and decision-making in biotech settings.

What cities near Shepherd, TX are hiring for Machine Learning Engineer Biotech jobs? Cities near Shepherd, TX with the most Machine Learning Engineer Biotech job openings:

Senior Machine Learning Engineer, Computer Vision, HD Map and SLAM

Bot Auto

Houston, TX โ€ข On-site

$99K - $137K/yr

Full-time

Posted 12 days ago


Job description

Company Introduction
At Bot Auto, we are revolutionizing the transportation of goods with our cutting-edge autonomous trucks, enhancing the quality of life for communities around the globe. With the agility of a start-up and the wisdom of seasoned experts, Bot Auto boasts a team that has achieved numerous world-firsts and unparalleled innovations. United by a shared vision, we create miracles and propel the future of transportation. Join us and transform your dreams into reality.
Key Responsibilities
  • Explore and propose new ideas using your knowledge and experience in deep learning, neural networks and large foundation models in autonomous driving including: end-to-end object detection, tracking and prediction, end-to-end planning and control, and end-to-end autonomous driving system, end-to-end online mapping. SLAM in localization and etc..
  • Work on the entire life cycle of machine learning projects from data analysis, model experimentations to performance metrics verifications, and understand the entire workflow in great detail.
  • Be exposed to many cross-team projects and collaborate with the product, simulation and other sibling autonomous driving algorithm teams to extend machine learning technology to all components.
Qualifications
Required:
  • Have an advanced degree (Ph.D or Master's) in related fields of study: computer science, computer engineering, robotics, mathematics, physics, and etc.
  • Have in-depth knowledge and extensive experience in machine learning, and/or computer vision, modern transformer architecture, and employ SOTA techniques of machine learning.
  • Be familiar with PyTorch, TensorFlow and other machine learning platforms and tools.
  • Have strong motivation to work independently in a fast paced environment while collaborating with other teams on more complex and larger projects.
Preferred:
  • Have a proven track record of research publications in top conferences and/or journals as the first author.
  • Have knowledge and experience of generative models, model distillation, or model inference acceleration (e.g. TensorRT) techniques.