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Internship Medical Data Encoder Jobs in Berkeley, CA

Rainfall Ventures

Controls Engineer

San Francisco, CA · On-site

$100K - $200K/yr

This is not a paper-reading internship. You'll ship code that runs on real hardware, debug edge ... in the loop data collection framework. Benefits * Medical, dental & vision plans ($0 payroll ...

Abbott

2027 Finance Internship

Alameda, CA

$20 - $26.25/hr

Connect with us at abbott.com, on LinkedIn at and on Facebook at General Medical Devices Our ... accurate data to drive better-informed decisions. We're revolutionizing the way people monitor ...

Abbott

2027 Finance Internship

Pleasanton, CA

$19.75 - $25.75/hr

Connect with us at abbott.com, on LinkedIn at and on Facebook at General Medical Devices Our ... accurate data to drive better-informed decisions. We're revolutionizing the way people monitor ...

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How much do internship medical data encoder jobs pay per hour?

As of Sep 14, 2026, the average hourly pay for internship medical data encoder in Berkeley, CA is $27.56, according to ZipRecruiter salary data. Most workers in this role earn between $21.20 and $30.00 per hour, depending on experience, location, and employer.

What is an internship medical data encoder?

An Internship Medical Data Encoder is a student or recent graduate who assists in converting medical information from patient records into standardized codes used for billing, insurance, and data analysis. This role is typically an entry-level position within healthcare organizations, offered as part of a practical learning experience. Interns learn to use coding systems such as ICD-10 and CPT, work with electronic health records, and ensure the accuracy and confidentiality of patient data. The internship provides valuable hands-on experience and can be a stepping stone to a full-time medical coding or health information management career.

What are the key skills and qualifications needed to thrive as an internship medical data encoder?

To thrive as an Internship Medical Data Encoder, you need attention to detail, strong organizational skills, and a basic understanding of medical terminology, often supported by coursework in health information management or related fields. Familiarity with electronic health record (EHR) systems, medical coding software (such as ICD-10 or CPT codes), and data entry tools is typically required. Excellent communication, time management, and the ability to maintain confidentiality are key soft skills for this position. These competencies ensure accurate data processing, support healthcare operations, and uphold patient privacy standards.

What are some common challenges faced by an internship medical data encoder, and how can they be addressed?

Internship Medical Data Encoders often encounter challenges such as accurately interpreting complex medical terminology and ensuring that data is entered consistently according to specific coding standards. Managing large volumes of patient records while maintaining a high level of attention to detail can also be demanding. To address these challenges, interns should familiarize themselves with coding guidelines, seek clarification from supervisors when uncertain, and utilize available reference materials and software tools. Regular feedback and collaboration with experienced team members can further help interns develop their accuracy and efficiency.

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Internship Medical Data Encoder jobs near you
Infographic showing various Internship Medical Data Encoder job openings in Berkeley, CA as of September 2026, with employment types broken down into 1% As Needed, 74% Full Time, 19% Part Time, and 6% Contract. Highlights an 91% Physical, 1% Hybrid, and 8% Remote job distribution, with an average salary of $57,315 per year, or $27.6 per hour.

Research Scientist - Vision Foundation Models

San Francisco, CA • On-site

Epsilon Labs, Inc.
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This job post has expired today. Applications are no longer accepted.

Job description

About Us

We're tackling one of healthcare's most critical challenges in medical imaging and diagnostics. Our company operates at the intersection of cutting-edge AI and clinical practice, building technology that directly impacts patient outcomes. We've assembled one of the industry's most comprehensive and diverse medical imaging datasets and have a proven product-market fit with a substantial customer pipeline already in place.

Role Overview

We're seeking a Research Scientist with deep expertise in vision foundation models to join our ML Research team. You'll be at the forefront of developing and deploying state-of-the-art vision models for medical imaging applications. This role focuses on pretraining and scaling vision encoders for radiology diagnosis across X-ray, CT, and MRI, with a growing emphasis on 3D volumetric modeling. You'll work with one of the largest and most diverse medical imaging datasets in the industry, pushing the boundaries of what's possible in AI-assisted diagnosis while maintaining the rigor required for clinical deployment.

Key Responsibilities

  • Design, train, and scale vision foundation models for radiology applications across X-ray, CT, and MRI modalities, implementing self-supervised, contrastive, masked image modeling, and joint-embedding predictive (JEPA) frameworks.

  • Extend 2D pretraining recipes to volumetric CT and MR data, addressing long sequence lengths, anisotropic spacing, and multi-sequence studies.

  • Evaluate model performance rigorously across academic benchmarks, internal offline datasets, and live production data.

  • Contribute hands-on to all stages of model development including dataset curation, architecture design, distributed training, and production deployment.

  • Stay current with cutting-edge research in computer vision and medical imaging AI.

  • Drive research and technical excellence through conference publications and technical blog posts, establishing best practices for training robust medical imaging models at scale.

Qualifications

  • 6+ years of academia/industry experience in computer vision/machine learning

  • Deep expertise in training vision encoder models at scale (e.g. ViT, ConvNeXt). Strong foundation in self-supervised pretraining, including contrastive, masked image modeling, self-distillation, and JEPA-style objectives.

  • Experience training on volumetric or spatiotemporal data (video, 3D medical imaging)

  • Track record of implementing complex models from research papers and adapting them to new domains

  • Proficiency in PyTorch or JAX, with experience training models on multi-GPU/distributed systems

  • Hands-on experience with medical imaging applications, particularly radiology (X-ray, CT, MRI)

  • Strong software engineering skills and ability to write production-quality code

Preferred Qualifications

  • Publications at top-tier conferences (CVPR, ICCV/ECCV, NeurIPS, ICLR, MICCAI)

  • Experience with 3D medical image processing and retrieval tasks

  • Familiarity with CT and MR acquisition (windowing, multi-sequence protocols, voxel spacing)

  • Experience with long-context training techniques (sequence parallelism, efficient attention)

  • Knowledge of vision-language models and multimodal learning

  • Experience with model interpretability and explainability methods

  • Understanding of clinical evaluation metrics, clinical workflows, and healthcare data (DICOM, HL7, etc.)

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