Sr. Full-Stack Engineer (Sales tech)

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

Benefits offered by this job

Meaningful ownership in a fast-growing company

Job summary

A leading tech company is looking for a Sr. Full-Stack Engineer to work in a hybrid environment based in San Francisco, New York, or Vancouver. The role involves building AI-native software by unifying data from various sources, designing smart agent systems, and delivering metrics that measure success. The ideal candidate has over 4 years of experience in application ownership and is proficient in React and Python. Salary ranges from $100,000 to $200,000 depending on experience, alongside equity opportunities.

Qualifications

• 4+ years building & owning applications end-to-end with measurable business impact.
• Experience stitching messy, multisource data for product reasoning.
• Familiarity with distributed systems at scale is a plus.

Responsibilities

• Ingest and unify data from multiple sources into a context graph.
• Design planner/executor patterns with evaluation frameworks.
• Build native surfaces in collaboration with product and GTM teams.
• Define success metrics with product and customers for fast iteration.
• Optimize accuracy, latency, and cost for agent runs.

Skills

React

Python

Cloud infrastructure

Data integration

Product ownership

Tools

FastAPI

GraphQL

PostgreSQL

DynamoDB

AWS

Kubernetes

Pulumi

Spark

Databricks

Job description

About the job Sr. Full-Stack Engineer (Sales tech)

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

Work type: 3 days in office, 2 days remote

Our client is building a collective intelligence layer for revenue, transforming messy, real-world data into context that AI agents can learn from and act on autonomously. The goal: replace meta-work with actionable insights that drive higher revenue per representative time digging through dashboards, and more time moving the pipeline forward.

If you're entrepreneurial and want to define what AI-native software looks like, how data becomes context, how agents plan and act, and how chat translates to measurable outcomes, you'll thrive here. The team ships in tight loops, learns in public, and measures success by customer impact. Backed by leading investors and trusted by customers like BigID and Modern Health, our client is shaping the future of AI-powered revenue software.

What You'll Do

• Create the collective memory: Ingest and unify data from multiple sources (CRM and beyond) into a semi-structured context graph that models winning deal patterns 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 actions, with simple evaluation frameworks to measure quality.
• Deliver where users work: Build native surfaces (apps, chat, and integrations) in close collaboration with product and GTM teams, reducing context switches and meta-work.
• Prove outcomes: Define success metrics (e.g., auto-completed tasks, adoption, retention, pipeline lift) with product and customers, while ensuring strong observability for fast iteration.
• Balance cost & reliability: Optimize accuracy, latency, and cost for agent runs and retrieval. Design safeguards and fallback mechanisms for dependable real-world performance.

What You'll Bring

• Owner/builder mindset with product taste you frame problems, choose the simplest path, and own outcomes.
• 4+ years building & owning applications 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, multisource data into something a product can reason over; strong instincts for reliability, privacy, and multitenant boundaries.
• Able to hit the ground running with React, 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 Our Client Works

• Outcome first. We anchor on the seller's 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.

React/TypeScript, Python, FastAPI/GraphQL, PostgreSQL/DynamoDB, AWS, Kubernetes, Pulumi, Spark/Databricks, and event-driven architectures. Familiarity helps, but isn't required.

Offer & Logistics

Base Salary: $100,000 $200,000 (depending on experience)

Equity: Meaningful ownership in a fast-growing company