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Full stack software engineer

San Francisco,...
D

Delty

Startup
Category
Technical
Experience
0-3 years
Type
Contract
Location
San Francisco, CA, US / Remote
Salary
$80,000 - $150,000
Job Description
Benefits & Culture
Flexible hours
Remote work
Paid time off
Fast-paced environment
Direct feedback
Growth opportunities
This goes straight to the founder
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Founder Signals
Response Rate
85%
Avg Response Time
2 hours
Compensation Calculator
In startups, higher salary usually means lower equity.
Salary (affects equity %)
Link salary ↔ equity
$125,000
$80,000$150,000
Equity % (auto from trade-off model)
0.75%
0%5%
Projected Exit Value
$350,000,000
$10M$500M
Live Trade-off
Salary
Equity %
Est. Equity Value at Exit
$100,000
1.00%
$1,742,500
$125,000
0.75%
$1,317,500
$150,000
0.51%
$892,500
$175,000
0.51%
$892,500

Advanced Salary & Equity Calculator

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About Us

Delty is building the world’s first AI Staff Engineer. Unlike typical code-generation tools, Delty is trained on a team’s codebase, documentation, and system history — giving it a system-level understanding of architecture, conventions, and constraints. Delty helps engineering teams design enterprise-scale software systems, make architectural decisions, and enable AI coding agents to work with real system context.

Delty was founded by former engineering leaders from Google, including co-founders with deep experience at YouTube and in large-scale infrastructure. You’ll get to work alongside people who built massive systems at scale — a chance to learn a lot and contribute meaningfully from day one.

We believe in solving hard problems together as a team, iterating quickly, and building software with long-term thinking and ownership.

What You’ll Do

  1. Work full-stack: design and build features spanning front-end, back-end, data storage and processing.
  2. Build new product modules and services from scratch — or evolve existing ones — guided by context-aware system design.
  3. Work with AI and machine learning: integrate large-language models (LLMs), process large or long-form text data, apply traditional ML (e.g. regression, data pipelines), and build tooling around AI-driven flows.
  4. Make architectural decisions — choose frameworks, data models, APIs, storage solutions — balancing trade-offs between performance, scalability, maintainability, and complexity.
  5. Collaborate closely with co-founders and other engineers to translate product vision into a working, maintainable codebase.

What We’re Looking For

  1. At least 3 years of full-stack engineering experience, including substantial work with AI/ML.
  2. Strong skills across front-end, back-end, databases/data storage — and demonstrated ability to design end-to-end systems.
  3. Experience working with or integrating AI/ML — LLMs, data pipelines, long-form text processing, traditional ML like regression or statistical modeling.
  4. Good design sense and architectural thinking: you understand trade-offs (scalability vs complexity, speed vs maintainability) and can choose wisely based on constraints.
  5. Comfort working in a fast-paced startup-style environment: nimble, iterative, high ownership.
  6. Bonus: prior startup experience, or even having been a founder — we value entrepreneurial thinking, self-direction, and willingness to wear multiple hats.

Why join

  1. Learn from seasoned Google engineers: As former Google engineers who built systems at YouTube and Google Pay, we’ve operated at massive scale. Working alongside us gives you a chance to build similar systems and learn best practices, scale thinking, and software design deeply.
  2. High impact: At a small but ambitious team, your contributions will influence architecture, product direction, and core features. You will have real ownership and see the effects of your work quickly.
  3. Grow fast: We’re iterating rapidly; you’ll be exposed to the full stack, AI/ML pipelines, system architecture, data modeling, and product-level decisions — a fast-track to becoming a senior engineer or technical lead.
  4. Challenging and meaningful work: We’re tackling the hardest part of software engineering: bridging AI-generated prototypes and robust, scalable enterprise-grade systems. If you enjoy thinking deeply about systems and building reliable, maintainable foundations — this is for you.