Algorithm Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Engage in and lead the complete lifecycle of biosignal-based algorithm development for medical devices, covering specifications and requirements gathering, data curation and labeling, development, failure analysis, production, maintenance, and documentation.

• Choose, apply, and develop suitable methods for each challenge, including determining when to utilize deep learning or alternative techniques.

• Enhance internal deep learning and machine learning tools to boost team productivity.

• Introduce innovative model architectures and algorithmic strategies.

• Refine the codebase to promote reusability and facilitate rapid experimentation.

• Advance best practices for creating user-friendly, well-documented, and thoroughly tested algorithm implementations, including unit tests, continuous integration (CI), and non-regression testing.

• Present findings to key stakeholders and aid in the utilization of algorithms for client engagement.

• Assist with client-facing projects and influence the impact of algorithms on customers.

• Collaborate with data scientists, neuroscientists, engineers, and clinicians to define, build, deploy, and maintain machine and deep learning models for analyzing brain and biosignal data.

⛳️ Requirements

• Over 4 years of industry experience in machine learning and deep learning, particularly within health sciences or other regulated environments.

• Demonstrated success in deploying algorithms into production.

• Experience with digital signal processing (DSP) and statistical analysis.

• Proficiency in using PyTorch or other deep learning frameworks.

• Awareness of recent advancements in deep learning, including Transformer/ViT, large-scale modeling, and extensive model training.

• Understanding of software and ML engineering best practices, such as testing, version control, code reviews, documentation, Dockerization, CI/CD, and experiment tracking.

• Familiarity with biosignals, medical imaging data, or large time-series datasets, or a strong desire to learn more in this field.

• Capability to distill, discuss, and present complex technical subjects appropriately for both internal and external audiences.

• Enthusiasm for engaging in the full algorithm development lifecycle, including scoping, data wrangling, experimentation, formal validation, quality/regulatory documentation, production deployment, and client collaboration.

• Collaboration, open communication, and continuous feedback are vital for collective success.

🏝️ Benefits

• Equity

• Paid Time Off (PTO)

• Flexible asynchronous remote work practices

• Access to in-person office hubs located in Boston, New York City, and Paris