Staff Engineer (Sales tech)

Hiring Hub; San Francisco (CA); Hybrid; USD 100,000 - 200,000; Full time

Benefits offered by this job

Equity opportunities

High ownership in projects

Job summary

A forward-looking tech company is seeking a Staff Engineer for its AI-first revenue platform. The role focuses on building a context and orchestration layer to enhance decision-making through data integration and automation. The ideal candidate has extensive experience in backend systems and a solid understanding of cloud infrastructure. Compensation ranges from $100k to $200k based on experience, along with meaningful equity opportunities in a collaborative and senior team environment.

Qualifications

• 4+ years of experience building and owning backend/platform systems end-to-end with measurable business impact.
• Experience in data integration and creating reliable systems.
• Able to work with Python and cloud infrastructure.

Responsibilities

• Create the collective memory by unifying data into a context graph.
• Design planners and executor patterns to turn context into actions.
• Define success metrics with product and customers.

Skills

Backend/platform systems development

Data integration

Python programming

Cloud infrastructure

Tools

AWS

PostgreSQL

Kubernetes

FastAPI

React

Job description

About the job 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.

What you'll do

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.

What you'll bring

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.

How the team works

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

Logistics & offer

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

Equity: meaningful ownership in a fast-growing company

Team: small, senior; big surface area and ownership