Staff Engineer; Sales tech

Location:
Hybrid in San Francisco, New York City or Vancouver

Work type: 3 days in office, 2 days remote

Role:
Staff Engineer Build the context + orchestration layer for AI-first revenue software

Our client's team is building an AI-first revenue platform a collective intelligence layer that connects messy, real-world data into context that AI agents can learn from and act on autonomously. The goal is simple: replace busywork with action that compounds revenue per representative,less digging through dashboards and more time moving sales pipelines.

If you're entrepreneurial and want to help define what AI-native software looks like how data becomes context, how agents plan and act, and how chat turns into outcomes, this is the place to build. The team ships in tight loops, learns in public, and measures success by customer impact.

Create the collective memory. Ingest and unify data from many sources (CRM and beyond) into a semi-structured context graph that captures what leads to winning deals modeled at multiple levels 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 to measure quality.

Deliver where users work. Expose capabilities through native surfaces (apps, chat, and integrations) in tight loops with product and GTM reducing context switches and meta-work.

Prove outcomes. With product and customers, define success metrics (e.g. tasks auto-completed, adoption/retention, pipeline lift; keep latency in check) and wire observability so we can ship learn iterate quickly.

Balance cost & reliability. Tune accuracy, latency, and cost for agent runs and retrieval; design fallbacks and safeguards that keep the system dependable under real-world load.

Owner/builder mindset with product taste you frame problems, choose the simplest path, and own outcomes.

• 4+ years building & owning backend/platform systems end-to-end, with 01 wins and measurable business impact.

Curious by default; comfortable taking smart risks and turning fuzzy problems into shipped outcomes.

You talk in terms of impact and trade-offs; decide with ~70% info; turn ambiguity into simple, testable systems.

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

Able to hit the ground running with Python and standing up cloud infrastructure.

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

• Outcome-first. Anchor on the sellers job; stay close to customers; success = adoption, pipeline quality, time-to-value.

Ship small, learn fast. Start simple; instrument; iterate with sniff tests.

High trust, high ownership. Own problems end-to-end and make product-level decisions with the team.

Python, FastAPI/Graph

QL, Postgre

SQL/Dynamo

DB, AWS, Kubernetes, Pulumi, Spark/Databricks, and event-driven architectures plus React for product surfaces. Familiarity helps, but isnt required.

Base salary: $100-$200k (based on experience)

Equity: meaningful ownership in a fast-growing company

Team: small, senior; big surface area and ownership