Manager, Algorithms and Machine Learning

Beacon Biosignals is seeking an Algorithms Manager to lead a group within the ML and Biomarker group. The ML and Biomarker group is responsible for building, productionizing, and maintaining the algorithmic systems that power Beacon’s core capabilities, including machine learning model development, training and evaluation pipelines, biomarker development workflows, and the services that support reliable deployment and operation.

In this role, you will lead a team of 5-7 engineers and be accountable for the team’s execution, health, and results. You will partner closely with technical and cross-functional stakeholders to translate company priorities into a clear roadmap, sound technical decisions, and strong team performance. This role reports to the Algorithms Senior Manager.

You are expected to bring sufficient technical depth in machine learning and algorithm development to evaluate approaches, guide tradeoffs, identify risks, and support high-quality decisions.

At Beacon, cultural and scientific impact comes from people who lead by example. We look for colleagues whose work reflects curiosity, sound judgment, a bias toward simplicity, a self-service mindset, and deep empathy for teammates, stakeholders, users, and patients. We believe diverse teams build more robust systems and achieve higher impact.

Beacon’s asynchronous work practices support a strong remote experience, with in-person hubs in Boston, New York, and Paris. The Algorithms organization is distributed across American and European time zones. This role is located in the US, with a preference for candidates near our Boston or New York hubs.

What success looks like

As a successful manager at Beacon, you will:

• Lead a high-performing Algorithms group through hiring, coaching, performance management, and clear expectations around ownership and impact.


• Be accountable for the team’s delivery against goals, ensuring alignment with Beacon’s strategy, regulatory requirements, and quality standards.


• Support engineer in making technical decisions across model development, biomarker pipelines, evaluation, and production systems, supporting the team in making sound tradeoffs.


• Help your team scope out and prioritize tactical week-to-week work in direct connection with Beacon's wider strategic initiatives.


• Ensure your team's development roadmap is outcome-oriented, up-to-date, aligned with the company's objectives and other teams' priorities, and communicated effectively to other stakeholders within Beacon.


• Own project management responsibilities for team initiatives, including defining scope, managing timelines, and ensuring expectations are set both internally and externally.


• Develop, refine, and own team-based processes that allow for efficient execution, communication, and coordination.

What you will bring

• Experience managing a mid-sized ML or algorithm engineering team, with a track record of coaching, hiring, and growing individuals across levels while maintaining strong team health and accountability.


• Proven ability to lead teams through technically complex, ambiguous work with cross-functional dependencies, balancing short-term delivery with longer-term capability building.


• A strong depth of knowledge in algorithm/ML-model development, deploying production-ready ML systems, and model-maintenance to assess approaches, guide tradeoffs, and make sound decisions.


• Experience operating within established frameworks and improving team-level ways of working, including prioritization, planning, and execution, while pragmatically addressing delivery friction and scaling challenges.


• Strong communication and stakeholder management skills, with the ability to align partners around decisions, tradeoffs, and evolving plans, including in regulated or quality-sensitive environments.


• A bias toward simplicity, with the ability to introduce the right level of process, avoid unnecessary complexity, and continuously improve how the team works.


• Experience developing ML or algorithmic systems in regulated domains, with familiarity in regulatory submissions, including validation, reproducibility, documentation, and model governance requirements.