Machine Learning Engineering Manager

Salary: $200K - $265K Location Type: Remote Employment Type: Full-Time

About Us

Fieldguide is dedicated to establishing a new standard of trust in global commerce and capital markets. We achieve this by automating and streamlining the work of assurance and audit practitioners, with a specific focus on cybersecurity, privacy, and ESG (Environmental, Social, Governance). In essence, we build software for the professionals who enable trust between businesses.

We are a remote-first company, headquartered in San Francisco, CA, empowering our team to do their best work from anywhere. We are supported by leading investors including Bessemer Venture Partners, 8VC, Floodgate, Y Combinator, DNX Ventures, Global Founders Capital, Justin Kan, Elad Gil, and others.

Fieldguide values diversity in backgrounds and experiences. We believe that a diverse team is essential to building the future of audit and advisory. Our team is characterized by inclusivity, drive, humility, and a supportive environment. We are intentional and self-reflective about the culture we are building, seeking teammates who are not only skilled in their respective areas but also deeply committed to supporting each other's growth.

As an early-stage startup employee, you will have the unique opportunity to shape the future of business trust. We aim to simplify the lives of audit practitioners by reducing their workload by up to 50% and improving their work-life balance. If you share our values and enthusiasm for building a great culture and product, you will find a home at Fieldguide.

About the Role

As a Machine Learning Engineering Manager at Fieldguide, you will leverage your expertise in ML and generative AI, combined with strong people leadership skills, to drive our most critical AI initiatives. You will directly manage a team of approximately 5 machine learning engineers who collaborate across multiple product teams. Your role will involve guiding them in delivering groundbreaking ML features for the audit and advisory industry.

Reporting to the Head of Engineering, you will partner with product and design leaders to define our AI roadmap and ensure the team's work aligns with Fieldguide's business objectives. You will be accountable for your team's delivery, the technical quality of their work, and the long-term scalability of our AI systems. This is a significant opportunity to build and scale a high-impact ML team within a rapidly growing Series B-stage startup, while cultivating an environment that embodies Fieldguide's values and remote-first culture.

What You’ll Do

• Lead and mentor the ML team:
  • Manage a team of approximately 5 Machine Learning Engineers, providing coaching, mentorship, and support.
  • Foster an inclusive, psychologically safe team culture that promotes growth and high performance.
• Deliver AI-powered features:
  • Oversee the end-to-end delivery of ML-driven product features, with a particular emphasis on NLP and generative AI solutions, across multiple teams.
  • Ensure projects are executed with high quality, reliability, and on schedule.
• Strategic collaboration:
  • Work closely with Product Managers, Designers, and the Head of Engineering to plan roadmaps, define our AI strategy, and integrate ML capabilities into the product.
  • Align the team’s efforts with company goals and customer needs through cross-functional collaboration.
• Technical guidance:
  • Provide technical direction for ML architecture and design decisions.
  • Guide the team on best practices for building generative AI systems, including effective prompt engineering techniques and robust solution design, while empowering individual engineers to own implementations.
• Quality and evaluation:
  • Establish and champion frameworks for evaluating model performance and accuracy.
  • Ensure our generative AI models and features are rigorously tested, monitored, and continually improved based on clear metrics and user feedback.
• Hiring and team growth:
  • Participate in hiring processes to attract and onboard top ML engineering talent.
  • Set clear expectations for team members and help scale the team by refining our hiring, interview, and onboarding processes.
• Talent development:
  • Own the career development and performance management of team members.