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Biomedical Machine Learning Jobs (NOW HIRING)

Masimo Corporation

$120 - $150/hr

The AI/Machine Learning Engineer IIwill be part of the R&D team at Masimo with focus on design and ... Experience in applying AI/ML to biomedical data * Experience with computer vision and/or robotics

Masimo

Sr Engineer, AI/Machine Learning

Irvine, CA · On-site

$140K - $170K/yr

Responsible for algorithm design, development, implementation, testing, and documentation for Biomedical Signal Processing systems * Design, train and evaluate machine learning models using large ...

Masimo

Sr Engineer, AI/Machine Learning

Irvine, CA · On-site

$140K - $170K/yr

Responsible for algorithm design, development, implementation, testing, and documentation for Biomedical Signal Processing systems * Design, train and evaluate machine learning models using large ...

CoVar

Machine Learning Engineer

Mclean, VA · On-site

$83K - $111K/yr

Machine Learning Engineer Location: McLean, VA (hybrid); occasional travel to Durham, NC and ... Our teams build AI/ML solutions that help the DoD detect enemies and threats, help biomedical ...

Masimo

Engineer II, AI/Machine Learning

Irvine, CA

$120K - $150K/yr

The AI/Machine Learning Engineer II will be part of the R&D team at Masimo with focus on design and ... Experience in applying AI/ML to biomedical data * Experience with computer vision and/or robotics

Masimo

Engineer II, AI/Machine Learning

Irvine, CA · On-site

$120K - $150K/yr

The AI/Machine Learning Engineer II will be part of the R&D team at Masimo with focus on design and ... Experience in applying AI/ML to biomedical data * Experience with computer vision and/or robotics

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Biomedical Machine Learning information

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How much do biomedical machine learning jobs pay per hour?

As of Sep 7, 2026, the average hourly pay for biomedical machine learning in the United States is $28.53, according to ZipRecruiter salary data. Most workers in this role earn between $24.28 and $32.21 per hour, depending on experience, location, and employer.

What is a biomedical machine learning?

A Biomedical Machine Learning job involves developing and applying machine learning algorithms to analyze biomedical data for healthcare and research applications. Professionals in this field work with medical imaging, genomics, electronic health records, and wearable device data to improve disease diagnosis, treatment, and patient outcomes. They collaborate with researchers, clinicians, and data scientists to design predictive models and extract insights from complex biological data. This role requires expertise in machine learning, data processing, and domain-specific knowledge in healthcare or life sciences.

What does a biomedical machine learning do?

A typical day in Biomedical Machine Learning involves cleaning and preparing biomedical datasets, developing or refining machine learning models, running experiments, and interpreting results in collaboration with domain experts such as bioinformaticians and clinicians. Professionals often participate in team meetings to discuss project goals, share insights, and adjust research directions based on feedback. The role may also involve reading scientific literature to stay current with new methodologies and contributing to academic publications or technical documentation. Working closely with both technical and healthcare-focused colleagues, you'll help translate data-driven insights into meaningful biomedical solutions that impact patient care or research outcomes.

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

To thrive in Biomedical Machine Learning, you need a solid background in statistics, machine learning, programming (Python or R), and a strong understanding of biological or medical data, often supported by advanced degrees in computer science, biomedical engineering, or related fields. Experience with frameworks like TensorFlow, PyTorch, and familiarity with biomedical datasets is highly valued, and certifications in data science or biomedical informatics can be advantageous. Strong analytical thinking, communication skills, and the ability to collaborate with interdisciplinary teams are crucial soft skills. These competencies are vital to developing robust models that address complex healthcare challenges while ensuring scientific rigor and regulatory compliance.

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Biomedical Machine Learning jobs near you
Infographic showing various Biomedical Machine Learning job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 24% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $59,333 per year, or $28.5 per hour.

Machine Learning Engineer - Drug Discovery

Astrix Inc

South San Francisco, CA • On-site

$60 - $70/hr

Full-time, Contractor

Posted 23 days ago

Job description

Pay Rate Low: 60 | Pay Rate High: 70
Our client is a leading biotech company seeking a highly motivated AI/ ML Scientist to join their innovative research organization focused on applying artificial intelligence and machine learning to drug discovery and molecular design.
Title: Machine Learning Scientist - Drug Discovery
Location: Remote - United States (PST preferred)
Schedule: Full-Time, 40 hours/week
Contract Duration: 12 months, with a strong possibility of extension
Employment Type: W-2 + Benefits
Compensation: $60-$70/hour, depending on experience and qualifications
Job Details:
This role will focus on designing, developing, training, and deploying advanced machine learning models and computational engines that support lab-in-the-loop molecular design and optimization. Areas of focus include sequence modeling, molecular structure, conformational ensembles, molecular property prediction, natural language processing, computer vision, and robotics. The successful candidate will work in a highly collaborative, multidisciplinary environment alongside ML scientists, ML engineers, computational scientists, and drug design experts to develop next-generation solutions at the intersection of AI and life sciences.
Key Responsibilities
  • Design, develop, optimize, evaluate, and deploy advanced deep learning models, including large language models, multimodal transformers, and generative AI models.
  • Build and optimize scalable data pipelines supporting machine learning and scientific applications.
  • Optimize model training and inference for performance, scalability, and accuracy using multi-GPU and cloud-based infrastructure.
  • Develop and maintain MLOps workflows covering model deployment, version control, monitoring, reproducibility, and ongoing model performance.
  • Develop machine learning approaches that connect diverse datasets, including genomics, transcriptomics, imaging, molecular, and clinical data.
  • Partner with scientists and engineers across disciplines to translate innovative machine learning methods into practical applications for drug discovery, disease research, and biomedical applications.
  • Independently troubleshoot complex modeling, software, and infrastructure challenges and drive solutions from development through deployment.

Qualifications:
  • B.S., M.S., or Ph.D. in Computer Science, Machine Learning, Computational Biology, Data Science, Statistics, Mathematics, or a related quantitative discipline.
  • Must be authorized to work in the United States without current or future employer sponsorship.
  • 1-5 years of relevant professional experience, including postdoctoral research where applicable.
  • Strong foundation in data structures, algorithms, software engineering, and computational problem solving.
  • Expert-level Python programming skills.
  • Extensive experience with deep learning frameworks such as PyTorch, JAX, or TensorFlow.
  • Strong debugging and software development skills, with the ability to independently diagnose and resolve complex technical issues.
  • Experience with large-scale or distributed model training, such as DDP, Ray, FSDP, or DeepSpeed.
  • Experience with model deployment technologies such as Triton or ONNX.
  • Experience working with cloud and/or GPU computing infrastructure.
  • Hands-on experience with geometric deep learning, molecular cofolding models, neural force fields, or related scientific ML approaches is highly preferred.

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