Founding AI Engineer - Manhattan Labs
At a glance
Mid-level AI Engineering role at Pear VC. Palo Alto · full-time · no salary stated · we estimate $132,450–$205,151 across the United States.
Pear VC has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Build RAG and agent-based systems that investigate financial records, measuring their accuracy, speed and cost
- Create backend services for document intake, case workflows and risk scoring, with human review built into the experience
- Make AI systems production-ready through evaluation, dataset tracking, controlled releases and fallback safeguards
- Secure and operate the platform with access controls, audit records, backups, monitoring and measurable service targets
- Set technical direction with the founders, and establish deployment, incident response and research practices
What you bring
- At least one year of technical project work; startup experience is strongly preferred
- Strong TypeScript and Python skills spanning current frontend and backend development
- You have shipped LLM integrations or RAG into production products used by customers
- You can extract entities and structured information from messy documents and work with APIs, relational databases and async jobs
- You have taken a product from local use to dependable production as it scales
Who this fits
This suits a hands-on founding engineer comfortable owning decisions across frontend, backend, infrastructure and data. You'll work directly with the founding team and help shape the architecture and product direction. The role is on-site in Palo Alto.
From the employer
About the team
We’re building the AI-native compliance platform for community banks and financial institutions.
Regulatory pressure is intensifying. Financial crime is becoming more sophisticated. Compliance teams are overwhelmed by manual workflows and fragmented systems. Community banks, despite managing billions in assets, lack the tooling to keep up.
We’re building the intelligence layer that changes that: a system that can answer questions like
“What is our exposure to this entity across all customers?”
- with a defensible, auditable answer.
Our team includes engineers and researchers from Stanford, Carnegie Mellon, and IIT Madras, and we’re backed by Tier 1 investors and experienced operators who understand the massive opportunity in modernizing financial crime compliance with AI.
Learn more at https://manhattanlabs.co/
.
Description of the role
This is a true founding engineer role. You won’t just ship features - you’ll define the system.
You’ll architect and build an AI-native platform that operates as the investigative brain for compliance teams. This includes agentic reasoning systems, large-scale document ingestion, and user-facing tools that must stand up to regulatory scrutiny.
You will take the product from early-stage MVP to a secure, reliable, enterprise-grade system used by financial institutions.
By reimagining KYB and AML as autonomous intelligence, you'll help build the platform that becomes the default compliance infrastructure for thousands of community banks, that each manage assets worth billions of dollars. You'll work directly with the founding team, own technical decisions, and lay the foundation for a potentially massive and fast-growing SaaS company.
Responsibilities and potential impact
Design, build, and operate RAG and agentic systems over complex financial data (corporate registries, sanctions lists, beneficial ownership, adverse media), with clear evaluation metrics for accuracy, latency, and cost
Develop scalable backend services for document processing (PDF/OCR/CSV), async job queues, case lifecycle orchestration, and risk scoring with confidence-driven UX and human-in-the-loop controls
Own the productionization of AI systems, including evaluation pipelines, dataset/version management, and safe rollout strategies (A/B testing, guardrails, fallback behavior)
Harden the platform for enterprise: auth controls, role-based access, audit trails, backups, and monitoring with measurable SLOs
Establish CI/CD, deployment pipelines, runbooks, and incident response practices from the ground up
Propose and engage in cutting-edge AI research related to our mission, particularly around agentic reasoning over regulatory and financial data
Work directly with the founding team to define architecture, technical roadmap, and product direction
Must haves
1+ years of experience on technical projects with startup experience strongly preferred
Strong TypeScript and Python skills across modern frontend and backend stacks
Hands-on experience shipping LLM integrations or RAG systems into production user-facing products
Experience with document processing, entity extraction, and structured data extraction from messy real-world sources
Solid grasp of API design, relational databases, and async job processing systems
Proven track record taking a product from "works locally" to reliable production at growing scale
Debugging instincts across frontend, backend, infra, and data boundaries - and an ownership mindset to match
Nice to haves
Knowledge of KYC/KYB/AML workflows, sanctions screening, beneficial ownership data, or regulated fintech
Experience with LangChain, LlamaIndex, or similar AI orchestration frameworks
Familiarity with banking APIs, core banking systems, or financial data providers
Familiarity with OCR and document intelligence pipelines
Background in fintech, compliance, RegTech, or other regulated industries
Cloud deployment experience and DevOps knowledge in security-sensitive environments
Strong product intuition and user experience focus
What we offer
We believe in generous, top-tier compensation and benefits for our employees:
Competitive salary and equity
100% health, dental, and vision coverage
Commuter stipend
Regular team events and off-sites