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Buoy Software
United States · remote · Salary not listed
As an Engineering Manager at Buoy Software, you lead a fully-distributed team of 6-8 engineers building high-quality software that powers life-saving operations in the blood collection industry. Partnering with product managers who bring project requirements to the team, you own the delivery of that software from requirement through to how it performs in Production.
You view your team as your primary product. You have the technical credibility to help engineers make sound decisions, but your time is spent safeguarding the team's well-being, recruiting strong talent, and setting the team up to deliver. You partner day-to-day with product managers and your team's tech leads, align with engineering leadership (your VP of Engineering and peer EMs) on strategy and priorities, and connect your team to the broader business context and company goals, all while fostering collaboration, ownership, and continuous improvement.
Apply on source siteListed via Himalayas
Above all, you are a problem solver who takes action rather than waiting for direction.
Take product requirements and deliver them through planning, estimation, team resourcing, and execution, helping engineers make sound technical and implementation decisions that balance the current build against future scale.
Secure clear team commitments, then communicate deliverables (and any changes to them) proactively to stakeholders.
Own software delivery end to end: all the way to Production and how it performs once it's there.
Prepare releases and manage a per-version release timeline and commitment. Pull in the right teams and reviewers, and keep stakeholders informed on where things stand and where they need to be.
Own issues that arise in Production. Communicate the root cause, get root cause analyses documented, drive resolution, and ship patches as needed.
When issues surface, manage up so the right people know early and are never surprised.
Manage a distributed team of engineers, holding regular 1:1s for feedback, support, and career guidance.
Set clear goals and outcomes, and help people grow through direct, constructive feedback.
Hold your team accountable when things slip or when improvement is needed.
Keep your team informed by sharing project and company direction so no one is working without context.
Conduct performance reviews and monitor team health and morale, adjusting to keep engagement high.
Set and document the team's norms for working with AI coding assistants and agents: the review discipline for AI-generated code, the bar for prompt quality in shared codebases, and the team's stance on AI-generated tests. Hold AI-written code to the same standard as a junior engineer's PR.
Close the adoption gap. Get the whole team productive with AI, not just a few power users, by coaching on both what these tools do well and where they fail.
Make the case with evidence. Track where AI is actually improving delivery and quality, and be honest about where it isn't, so investment and tooling decisions are grounded in outcomes rather than hype.
Keep AI use safe for a regulated domain: no unreviewed generated code in Production, clear ownership of what shipped, and traceability for how it got there.
Actively seek, interview, and hire strong engineers, and continuously improve the onboarding experience for new hires.
Work with tech leads to refine engineering processes and practices, run effective stand-ups, iteration planning, backlog refinement, and retrospectives, and remove impediments blocking the team.
Champion initiatives that reduce technical debt and improve the architecture and scalability of the systems.
Translate technical detail (written or verbal) into plain, relatable language for non-engineers, whether in meetings, release notes, or emails. No jargon.
Communicate proactively with cross-functional partners and stakeholders. If someone doesn't know where something stands (project status, an implementation detail, missing context), you are the one who makes it clear.
Manage up to your own manager: keep them informed on progress, risks, and resource needs so they can advocate for the team, and provide upward feedback that shapes company-wide practices.
Ensure coverage for you or your team, and ask for help when you or your team needs it.
A self-driven go-getter. You are honest, transparent, feel empowered to act, and want to make things better.
A problem solver. When a mistake is made or an improvement is needed, you don't sit back waiting for documentation to be written or fixes to appear. You dig into the situation, take action, and own the longer-term improvement. Even when you or your team isn't the right hands-on person to solve something, you bring the idea to the people who are.
An AI-forward practitioner. You use AI in your own work day to day, and you help your team get more leverage from these tools without lowering the bar on quality or accountability.
A clear communicator. Strong written and verbal skills, and the judgment to tailor technical information to any audience.
An adaptable manager. You lead engineers with skill sets different from your own, set clear expectations, give frequent constructive feedback, and advocate for your team's growth.
4+ years in software engineering, including 2+ years in a leadership or mentoring role.
Proven track record of managing engineering teams and delivering impactful, high-quality products.
Deep understanding of software development practices, especially web application development. Experience with Ruby on Rails, AWS, and modern CI/CD pipelines is a plus.
Strong grounding in system design, architecture, and engineering best practices, with credibility in code reviews and technical mentorship.
Experience with Agile methodologies and project management tools.
Hands-on fluency with AI development tools (for example Cursor, GitHub Copilot, or Claude Code), with the judgment to know when to trust AI output and when to override it. You've reviewed enough AI-generated code to recognize how it fails.
A passion for developing people, grounded in empathy and clear communication.
Strong conflict resolution and problem-solving abilities.
Ability to balance team needs with business objectives.
Get up to speed on Buoy's product suite, tech stack, and org structure including how the team currently uses AI tools and where adoption is uneven.
Build relationships with your direct reports and peers across engineering, product, and leadership.
Understand ongoing projects, goals, challenges, and team dynamics, and hold 1:1s to learn each team member's role and career goals.
Shadow engineering meetings to learn our processes and spot areas to improve.
Take ownership of your team's ongoing projects, ensuring smooth execution and delivery.
Give feedback on current engineering processes and suggest efficiency improvements.
Build a clear picture of technical debt and start contributing to efforts that address it.
Establish or refine the team's working norms for AI-assisted development, and confirm they satisfy Buoy's security and compliance requirements.
Work with product managers to help prioritize features and technical initiatives.
Start contributing to hiring, assisting in interviews and decisions.
Fully own your team's performance, driving accountability, productivity, and quality.
Implement initiatives that address key challenges you've identified in the team's workflow or architecture.
Drive cross-team collaboration and contribute to larger strategic projects.
Deliver your first read on AI's impact on the team, where it's helping delivery and quality and where it isn't, and use it to recommend where the team invests next.
Help shape the long-term engineering roadmap alongside senior leadership.
Originally posted on Himalayas