Senior Machine Learning Engineer

Job Title: Senior Machine Learning Engineer (Computer Vision)

Location: Washington DC  (Hybrid, 1 day a week in office)

Note

**Applicants must possess a minimum of 5 years of hands-on experience in a production (non-research focused) environment, excluding internships, to be considered for this position.**

Company Description:

Over 4 million postoperative deaths happen each year worldwide.  Surgical Data Science Collective(SDSC) is non-profit organization aiming to reduce those numbers by using  the latest advances in computer vision and machine learning to analyze surgical videos, after surgeries have been conducted. We use these videos to provide feedback to our users on how they can improve their surgical technique and enhance the overall outcome of their surgeries. Our mission is to make surgery safer and more efficient through technology that advances the field to a greater level.

Position Summary:

We are looking for a highly skilled Senior Computer Vision/Machine Learning Engineer that has experience or education in image processing, machine learning, video coding & compression, and system integration. Someone with experience developing deep learning algorithms using TensorFlow, Keras or Pytorch, and experience with OpenCV. You will report to the Director of Machine Learning.

Responsibilities:

• Designing and developing innovative computer vision and machine learning algorithms specialized in surgical video analysis.

• Working with large datasets, designing and implementing data pipelines, and evaluating performance of different computer vision approaches.

• Developing and implementing image processing techniques including feature extraction, segmentation, and tracking.

• Collaborating with researchers and clinical partners to translate requirements into technical solutions.

• Create and produce technical documentation, including detailed technical designs, end user guides, workflows, and training materials.

• Continuously researching new and emerging technologies and tools in computer vision and machine learning to enhance the product and service offering.

Qualifications:

• 5+ years of experience in production environments.

• Bachelor’s or Master's in Computer Science, Electrical Engineering, or related-tech fields.

• Experience in computer vision, machine learning, and deep learning engineering

• Strong programming skills with experience using programming languages such as Python, C++, or MATLAB. Experience with OpenCV is required.

• Experience with machine learning frameworks and libraries such as Pytorch, TensorFlow, Keras.

• Experience with MLOps tools such as ClearML and wandb.

• Strong mathematical and algorithm design skills. Experience with image processing using CNN, RNN, LSTM, ViT and other deep learning neural network types.

• Strong experience with AWS Sagemaker and Lambda.

• Extensive experience in building and validating image and video processing pipelines and integrating with large data repositories.

• Strong understanding of software development process; experience with source control, bug tracking, and build management.

• Ability to work independently, self-starter, and manage projects from conception to deployment.

Nice to haves:

• Any experience with Vision transformers

• Experience working with analyzing medical images of any kind.

• Startup experience.

We are an equal opportunity employer committed to diversity, equity, and inclusion. We offer a competitive salary package, 401k, health insurance, flexible work arrangements, and a creative, dynamic work environment with passionate colleagues.

About us: The Surgical Data Science Collective (SDSC) is a nonprofit on a mission to unlock the power of surgical data. We bring together surgeons, scientists, and engineers to turn surgical videos into searchable, data-rich tools. Using AI, we help uncover insights that improve technique, sharpen decision-making, and elevate patient care. From smarter metrics to secure video libraries, we give surgical teams the tools to ask better questions—and find better answers. Because when surgeons get better, patients do too.