# Senior Algorithm Engineer

**Company:** [Beacon Biosignals](https://hotfix.jobs/companies/beacon-biosignals)
**Location:** Remote
**Role:** ML Engineering
**Experience:** 5+ years
**Skills:** Machine Learning, Deep Learning, Digital Signal Processing, Statistics, PyTorch, Transformers, Vision Transformers, Docker, CI/CD, Experiment Tracking, Python, Biosignals, Medical Imaging, Time-Series Analysis
**Posted:** 2026-08-08

> Build, validate, deploy, and maintain machine and deep learning algorithms for biosignal and brain data used in medical devices and precision medicine. The role requires 4+ years of industry experience, production ML expertise, DSP and statistics knowledge, and proficiency with PyTorch or comparable frameworks.

## Job Description

## Responsibilities
- Lead the full biosignal-based algorithm development lifecycle for medical devices, including requirements gathering, data curation and labeling, development, failure analysis, production, maintenance, and documentation.
- Select, implement, and develop appropriate methods for each problem, including deep learning and non-machine-learning approaches.
- Improve internal machine learning and deep learning tools, introduce model architectures and algorithmic techniques, and refine the codebase for reusable rapid experimentation.
- Establish best practices for user-friendly, documented, and tested algorithm implementations, including unit tests, continuous integration, and non-regression testing.
- Present results to stakeholders and help them use algorithms in client engagements.
- Support client-facing projects and assess the impact of deployed and future algorithms.

## Requirements
- More than 4 years of industry experience in machine learning and deep learning, particularly in health sciences or other regulated fields, with a record of bringing algorithms into production.
- Experience with digital signal processing and statistics.
- Proficiency with PyTorch or other deep learning frameworks for training, developing, and deploying deep learning models.
- Familiarity with current deep learning advances, including Transformers, Vision Transformers, large-scale modeling, and large-model training.
- Knowledge of software and machine learning engineering practices, including testing, version control, code reviews, documentation, Dockerization, CI/CD, and experiment tracking.
- Familiarity with biosignals, medical imaging data, or large time-series datasets, or enthusiasm for learning the domain.
- Ability to collaborate effectively and communicate complex technical topics to internal and external audiences.
- Willingness to participate in algorithm scoping, data wrangling, experimentation, formal validation, quality and regulatory documentation, production deployment, and client work.

## Compensation and Benefits
- Base salary is determined by experience, skills, and qualifications.
- Total compensation includes equity, paid time off, and other benefits.

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