[Hybrid/US] AI Founding Engineer (B2B Product & Frontend Experience)

Qualifications

• We are looking for an owner-builder who can take ambiguous problems to production, make clear trade-offs, and mentor others while staying hands-on
• Experience building B2B SaaS products (both frontend and backend) at the seed to Series C stage
• Ability to comfortably design and implement features on top of LLMs
• Strong Python; able to stand up cloud infrastructure and production services on AWS
• Experience stitching messy, multi-source data into models a product can reason over; strong privacy, reliability, and multi-tenant instincts
• Comfortable deciding with ~70% information, instrumenting what matters, and iterating
• Nice to have: exposure to agent orchestration/planning, retrieval or graph-shaped context, eval frameworks, and distributed systems at scale
• Staff-level IC from an AI/data/infra or GTM tooling company who has owned a platform surface from 0→1 and through reliability hardening
• Strong Python + AWS; comfortable with Postgres or DynamoDB, FastAPI/GraphQL, Kubernetes, Pulumi, event-driven design
• Experience with agent orchestration, retrieval, or graph-shaped context; adds evals and observability to ship quickly and safely
• Excellent instincts for multi-tenant isolation, privacy, reliability, and cost control
• Communicates trade-offs clearly; collaborates well with Product and GTM
• 2-3 interviews and technical assessment with the team + founders

Benefits

• Salary Range: $100,000 - $200,000 per year

Responsibilities

• As a Staff Engineer, you will lead the design and implementation of our context and orchestration layer, transforming messy GTM data into actionable outcomes with tightly-looped production system shipping
• As a staff IC you will lead design and implementation of the context and orchestration layer, ship production systems in tight loops, and set engineering standards for reliability, latency, and cost
• You will partner daily with Product and GTM to turn messy GTM data into shipped outcomes
• Create the collective memory: ingest and unify multi-source data into a multi-level context graph with strong tenant isolation
• Orchestrate agentic systems: design planner/executor patterns, tools, and policies (including MCP-style interfaces) that turn context into content and then into actions; define simple eval harnesses
• Deliver where users work: expose capabilities through native surfaces (apps, chat, integrations) to reduce context switches and meta-work
• Prove outcomes: define success metrics (tasks auto-completed, adoption/retention, pipeline lift) and wire observability so we can ship → learn → iterate quickly while meeting latency targets
• Balance cost and reliability: tune accuracy, latency, and run cost for retrieval and agents; implement fallbacks and safeguards for real-world load
• Raise the bar: write RFCs, lead design reviews, mentor peers, and improve code quality, SLOs, and on-call practices

Full Description

Salary Range: $100,000 - $200,000 per year Hybrid 3 days/week in SF, NYC, or Vancouver Role Overview As a Staff Engineer, you will lead the design and implementation of our context and orchestration layer, transforming messy GTM data into actionable outcomes with tightly-looped production system shipping.

Responsibilities

As a staff IC you will lead design and implementation of the context and orchestration layer, ship production systems in tight loops, and set engineering standards for reliability, latency, and cost. You will partner daily with Product and GTM to turn messy GTM data into shipped outcomes.

• Create the collective memory: ingest and unify multi-source data into a multi-level context graph with strong tenant isolation.

• Orchestrate agentic systems: design planner/executor patterns, tools, and policies (including MCP-style interfaces) that turn context into content and then into actions; define simple eval harnesses.

• Deliver where users work: expose capabilities through native surfaces (apps, chat, integrations) to reduce context switches and meta-work.

• Prove outcomes: define success metrics (tasks auto-completed, adoption/retention, pipeline lift) and wire observability so we can ship → learn → iterate quickly while meeting latency targets.

• Balance cost and reliability: tune accuracy, latency, and run cost for retrieval and agents; implement fallbacks and safeguards for real-world load.

• Raise the bar: write RFCs, lead design reviews, mentor peers, and improve code quality, SLOs, and on-call practices.

Qualifications

We are looking for an owner-builder who can take ambiguous problems to production, make clear trade-offs, and mentor others while staying hands-on.

• Experience building B2B SaaS products (both frontend and backend) at the seed to Series C stage.

• Ability to comfortably design and implement features on top of LLMs.

• Strong Python; able to stand up cloud infrastructure and production services on AWS.

• Experience stitching messy, multi-source data into models a product can reason over; strong privacy, reliability, and multi-tenant instincts.

• Comfortable deciding with ~70% information, instrumenting what matters, and iterating.

• Nice to have: exposure to agent orchestration/planning, retrieval or graph-shaped context, eval frameworks, and distributed systems at scale.

Ideal Candidate Profile • Staff-level IC from an AI/data/infra or GTM tooling company who has owned a platform surface from 0→1 and through reliability hardening • Strong Python + AWS; comfortable with Postgres or DynamoDB, FastAPI/GraphQL, Kubernetes, Pulumi, event-driven design • Experience with agent orchestration, retrieval, or graph-shaped context; adds evals and observability to ship quickly and safely • Excellent instincts for multi-tenant isolation, privacy, reliability, and cost control • Communicates trade-offs clearly; collaborates well with Product and GTM Process interview 2-3 interviews and technical assessment with the team + founders.

Initial screening by VP of Engineering.

About Company

We are a seed-stage, San-Francisco–based AI revenue system that turns fragmented GTM data into a collective sales memory and agent-driven workflows so reps prioritize winnable accounts and act with precision.

The platform combines Account Prioritization, Deep Research Agents, and territory/design tools; customer stories on the site include BigID, Modern Health, and ALTR.

Announced a $5.3M seed round led by SYN Ventures and highlights backing from operators such as ex-COO, Asana Stage/size: Seed-stage; small team Culture We are a seed-stage, San-Francisco–based AI revenue system that turns fragmented GTM data into a collective sales memory and agent-driven workflows so reps prioritize winnable accounts and act with precision.

The platform combines Account Prioritization, Deep Research Agents, and territory/design tools; customer stories on the site include BigID, Modern Health, and ALTR.

Announced a $5.3M seed round led by SYN Ventures and highlights backing from operators such as ex-COO, Asana