Leads engineering delivery for GTM systems, including Quoting and Catalog Management, guiding a team to build scalable solutions, release valuable features, and improve quality through stakeholder collaboration.
Founding Engineering Lead
π Description
- Build and lead an engineering pod, supporting team growth, performance, and hiring while shipping critical work.
- Own platform architecture, infrastructure decisions, and build-versus-buy choices.
- Set engineering standards for AI-assisted development, code review, releases, and on-call practices.
- Lead technical designs and initial implementations for new initiatives.
- Evaluate applied machine learning research for production readiness and partner on product roadmap and model strategy.
- Remain hands-on with implementation as leadership responsibilities grow over time.
π οΈ Requirements
- Typically 5 to 8 years of engineering experience, with 4 to 5 years considered for exceptional candidates.
- Commercial experience training and deploying machine learning models in production, beyond integrating third-party LLM APIs.
- Experience at an AI-native company, with technical leadership or ownership and a record of attracting strong engineering talent.
- End-to-end ownership of technical architecture, including infrastructure and cost decisions.
- Experience setting engineering standards and leading delivery practices for a team.
- Recent hands-on experience shipping production software, strong applied ML judgment, and effective use of AI coding agents.
- Comfort working through ambiguity, inherited code, and hands-on implementation.
β¨ Benefits
- Base salary: $200,000 to $400,000 USD annually. Visa sponsorship is not available.
Full job description
About the Role
Lead engineering for an early-stage SaaS company applying machine learning to enterprise software. You will shape technical architecture and product direction while leading a small engineering pod and remaining hands-on with critical builds.
What You'll Do
Build and lead a pod of engineers, supporting their growth, performance, and hiring while personally shipping critical work.
Own platform architecture and make build-versus-buy and infrastructure decisions.
Establish engineering standards for AI-assisted development, including review criteria, definition of done, release cadence, and on-call practices.
Lead technical designs and initial implementations for new initiatives.
Assess applied machine learning research and determine what is ready for production systems.
Partner with technical leadership on product roadmap, model strategy, and machine learning direction.
Initially spend most of your time building, with leadership responsibilities growing over time.
What We're Looking For
Typically 5 to 8 years of engineering experience, with 4 to 5 years considered for exceptional candidates.
Commercial experience training and deploying machine learning models in production, beyond integrating third-party LLM APIs.
Experience at an AI-native company, with technical leadership or ownership and a record of attracting strong engineering talent.
End-to-end ownership of technical architecture, including infrastructure and cost decisions.
Experience setting engineering standards and leading delivery practices for a team.
Recent hands-on experience shipping production software, strong applied ML judgment, and effective use of AI coding agents.
Comfort working through ambiguity, inherited code, and hands-on implementation.
Compensation & Benefits
Base salary: $200,000 to $400,000 USD annually. Visa sponsorship is not available.
Location
On-site in New York City, United States.
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