- You have more than 5 years of industry experience in machine learning and deep learning, particularly in health sciences or other regulated fields, with a proven track record of bringing algorithms into production.
- You are experienced with digital signal processing (DSP) and statistics and care about using the right tool for the job, which in many cases might not be machine learning or deep learning.
- You are proficient in using PyTorch (preferred) or other deep learning frameworks for training, developing, and deploying deep learning models.
- You are proficient with latest Deep Learning advances (Transformer/ViT, large scale modeling, large model training, ...)
- You follow and spread best practices in software and ML engineering, including testing, version control, code reviews, documentation, Dockerization, CI/CD, and experiment tracking.
- You are experienced with biosignals, medical imaging data, or large time-series datasets, or are enthusiastic about learning more in the domain.
- You thrive in a team environment, recognizing that collaboration, open communication, and continuous feedback are essential for collective success.
- You are able to distill, discuss, and present complex technical topics in a way that is appropriate for the audience at hand, both internally and externally.
- You are excited to participate in the entire algorithm development lifecycle, which spans scoping, data wrangling, algorithm development/experimentation, formal validation, quality/regulatory documentation, production deployment, and working with clients who might benefit from these algorithms.
The US-based salary range for this role is $170,000 – $220,000. Salary ranges are determined using current market compensation data for this role and adjusted based on experience, skills, and location. The base salary is one component of the total compensation package, which includes equity, PTO, and other benefits.
At Beacon, we've found that cultural and scientific impact is driven most by those that lead by example. As such, we're always seeking new contributors whose work demonstrates an avid curiosity, a bias towards simplicity, an eye for composability, a self-service mindset, and - most of all - a deep empathy towards colleagues, stakeholders, users, and patients. We believe a diverse team builds more robust systems and achieves higher impact.