Manager of Machine Learning Engineering, Fieldguide
$200-250k
Offers Equity and bonus
Software for assurance & advisory firms
Be an early applicant
Software for assurance & advisory firms
21-100 employees
Be an early applicant
$200-250k
Offers Equity and bonus
21-100 employees
Company mission
Fieldguide's mission is to increase trust in commerce and capital markets.
Role
Who you are
- Leadership and ML expertise: 8+ years of experience in applied machine learning (or related field), including at least 2+ years in a tech lead or people manager role. Proven ability to lead and develop engineering talent in a fast-paced environment
- Generative AI & NLP skills: Deep understanding of modern NLP, large language models (LLMs), and generative AI systems. Hands-on experience with prompt engineering for LLMs and developing evaluation frameworks to measure and improve model performance and quality
- Strong technical foundation: Proficiency in Python and ML libraries/frameworks, with a track record of building, deploying, and scaling ML solutions in production. Solid grasp of data engineering (ETL pipelines, data quality) and MLOps best practices for model deployment and monitoring
- People management excellence: Demonstrated ability to mentor, coach, and manage engineers effectively. Experience conducting performance reviews, providing constructive feedback, and guiding career growth for a technical team
- Exceptional communication: Excellent collaboration and communication skills. Comfortable working cross-functionally with product managers, designers, and other stakeholders to translate business needs into ML solutions and to clearly communicate complex concepts to non-engineers
- Strategic and analytical mindset: Adept at thinking big-picture about AI strategy while also able to dive into details. Strong problem-solving skills and decision-making judgment, especially in balancing quick wins vs. long-term research investments
- Culture and values alignment: Passion for building an inclusive, high-performing team culture. Embody Fieldguide’s values (Fearless, Fast, Lovable, Owners, Win-Win, Inclusive) in your leadership style, and excited to help shape a remote-first, mission-driven team
What the job involves
- As a Machine Learning Engineering Manager at Fieldguide, you will combine your expertise in ML and generative AI with strong people leadership to drive our most critical AI initiatives.
- You’ll directly manage a team of ~5 machine learning engineers working across multiple product teams, guiding them in delivering first-of-their-kind ML features for the audit and advisory industry.
- Reporting to the Head of Engineering, you will collaborate with product and design leaders to shape our AI roadmap and ensure the team’s work aligns with Fieldguide’s business objectives. You are responsible for your team’s delivery, technical quality, and long-term scalability of our AI systems.
- This is a unique opportunity to build and scale a high-impact ML team at a rapidly scaling Series B-stage startup, while fostering an environment that reflects Fieldguide’s values and remote-first culture
- Lead and mentor the ML team: Manage a team of ~5 Machine Learning Engineers, providing coaching, mentorship, and support. Foster an inclusive, psychologically safe team culture that enables growth and high performance
- Deliver AI-powered features: Oversee the end-to-end delivery of ML-driven product features (especially 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 in 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 loops 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 process for your team. Conduct regular 1:1s and performance reviews, deliver actionable feedback, and create growth plans that advance each engineer’s skills and career path
- Continuous improvement: Refine team processes and workflows for efficiency and quality (e.g. code reviews, research spikes, deployment practices). Advocate for resources or process changes needed for the ML team to excel, and ensure the team’s work remains innovative, impactful, and aligned with Fieldguide’s high standards
Our take
Fieldguide offers a cloud-native collaboration and automation platform for accounting and advisory practices. The idea is to make workflow and collaboration more streamlined, and automate parts of it altogether, for teams working on cybersecurity, risk, and ESG audits.
Fieldguide has launched with a focus on three high-demand and high-growth areas of business. With cybersecurity and ESG in particular seeing increased corporate investment, thanks to the steep costs of error or regulatory non-compliance, demand for tech that can help manage this influx of auditing work is likely to grow fast. Indeed, Fieldguide has received an impressive funding injection which will see the company looking to expand the kinds of audit their product applies to, as well as aggressively growing their team.
Fieldguide has already netted some key audit and advisory firms as clients and a substantial Series B funding round, suggesting that the young company is onto something good. Internal productivity tools are seeing a boom across multiple industries, with automation also seeing a significant uptake for enterprise businesses. If Fieldguide can successfully manage its scale-up, it could become an important software player.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
55% employee growth in 12 months
Company
Funding (last 2 of 3 rounds)
Mar 2024
$30m
SERIES B
Mar 2022
$17m
SERIES A
Total funding: $47.2m
Company benefits
- Health
- Dental
- PTO
Company HQ
Eureka Valley, San Francisco, CA
Leadership
Chris Szymansky
(CTO)Previously co-founded live video streaming platform Vivo, before moving on to work in software development at Fireman Creative. Subsequently held various high level positions, including CTO at JazzHR, and VP of Engineering, Product, and Design at Atrium.
Jin Chang
(CEO)Originally a Management Consultant at EY, before co-founding blockchain aggregation API company, Bitbutter. Moved on to serve as Chief of Staff at Atrium.
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