Original listing text, shown exactly as published by the company.
What you'll do
Own Technical Direction and Architecture
- Set the technical direction for AI systems - including shared AI SDKs, guardrails, evaluation frameworks, feedback systems, and agentic workflow infrastructure
- Own architecture and technical strategy for complex backend and AI platform systems, from design through production
- Lead technical design for ambiguous, cross-functional initiatives - evaluating tradeoffs, aligning stakeholders, and driving implementation
- Evaluate and select technologies with a bias toward what ships well and scales sustainably
Build and Operate AI Systems
- Write production code as a hands-on individual contributor - this is not a role that delegates implementation to others
- Design and operate LLM-powered systems: RAG pipelines, agentic workflows, evaluation infrastructure, guardrails, and model observability
- Own end-to-end reliability of AI systems from design through structured output delivery
- Define quality benchmarks, evaluation frameworks, and feedback loops to continuously improve AI output accuracy and system reliability
Champion AI-Native Development
- Champion and embed AI-native development practices and tools (e.g., Cursor, Augment) to achieve significant productivity gains across the team
- Foster a culture of rapid iteration, high velocity, and quality - including guiding the effective use of AI code generation
- Bring strong, informed opinions about how to get the most from AI-assisted development while maintaining reliability and correctness
Lead and Grow the Team
- Mentor engineers, raise the quality of technical decision-making, and help the team execute with consistency
- Establish coding standards, review practices, and architectural documentation that scale as the team grows
- Help define what "good" looks like for a team building at speed without sacrificing quality
- Partner with recruiting to build and grow the team
Collaborate Cross-Functionally
- Work closely with engineering managers, product, design, and QA to translate requirements into executable technical plans
- Participate actively in design reviews and roadmap discussions with grounded, implementation-level perspective
- Handle most cross-team conflicts and technical decisions autonomously
Qualifications
Required
- 7+ years of backend and/or ML engineering experience with a trajectory of increasing technical leadership, architectural responsibility, and mentorship
- A portfolio of shipped production systems - we will ask you to walk through specific technical decisions you personally made and code you personally wrote; this is not a role for someone whose primary contribution has been directing others
- Deep expertise in Python, with strong proficiency in building production-grade backend services; experience with other server-side languages (Node/TS, Java) a plus
- Production experience building and operating AI/LLM-powered systems — such as AI SDKs, RAG pipelines, evaluation frameworks, agentic workflows, or model observability — in real systems, not just experimentation
- Experience leading technical design for complex systems, including making architecture decisions, evaluating tradeoffs, and guiding implementation across multiple engineers or teams
- Experience with relational/NoSQL databases, including schema design, performance tuning, and data modeling
- Experience building and operating cloud-native systems using AWS, Docker, Kubernetes, and infrastructure as code
- Strong understanding of CI/CD, observability, reliability, and operational excellence in production environments
- Ability to work through ambiguity, break down complex problems, and drive alignment across engineering, product, and design
- Demonstrated ability to raise engineering quality through technical standards, design reviews, mentoring, and reusable platform patterns
- Hands-on experience with AI-native development tools (e.g., Cursor, Augment); demonstrated ability to embed AI-driven practices to accelerate team velocity and code quality
- Ability to critically evaluate AI-generated code and outputs, including identifying failure modes, regressions, and edge cases
Preferred
- Experience with document processing pipelines, structured extraction, or vector stores
- Familiarity with MLOps tooling, experiment tracking, or model deployment pipelines
- Background in financial document processing or fintech data pipelines
- Prior experience in a technical lead or TLM capacity on a new or early-stage product team
CompensationCompensation for this position includes a base salary, equity, and a variety of benefits. The U.S. base salary range for this role is $210,000 – $260,000 USD. Actual base salaries will be based on candidate-specific factors, including experience, skillset, and location, and local minimum pay requirements as applicable.
