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2026 Summer AI ML Engineer Intern

San Francisco,...
T

ThirdLayer

Startup
Category
Technical
Experience
4-7 years
Type
Internship
Location
San Francisco, CA, US
Salary
$6,000 - $10,000
Job Description
Benefits & Culture
Will sponsor visa
This goes straight to the founder
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No recruiters. Founders reply directly.

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
$6,000$10,000
Equity % (auto from trade-off model)
0.51%
0%5%
Projected Exit Value
$350,000,000
$10M$500M
Live Trade-off
Salary
Equity %
Est. Equity Value at Exit
$100,000
0.51%
$892,500
$125,000
0.51%
$892,500
$150,000
0.51%
$892,500
$175,000
0.51%
$892,500

Advanced Salary & Equity Calculator

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2026 Summer Research Engineer Intern

Join us in pushing the boundaries of what's possible with LLMs and browser-native AI. You'll work on cutting-edge problems in agent systems, context handling, and tool use, while collaborating directly with our research team to bring novel approaches to production.

What You'll Build

  1. Browser agent and tool-calling multi-agent systems.
  2. Evaluation frameworks for memory, efficiency, and accuracy.
  3. Memory and personalization layer for workflows

Requirements

  1. Research or work experience with RL environments, LLMs, modern AI frameworks, and/or ML.
  2. Experience with prompt engineering strategies.
  3. Strong foundation in Python and TypeScript.

Sample Projects

  1. Designing and creating tool-calling environments to evaluate and benchmark agent systems
  2. Agentic systems that predict and execute users’ next steps in complex workflows.
  3. Mapping user paths on real world software to API functionality and action trajectories.
  4. A searchable, self-updating memory store for continuously learning agents.
  5. A system to interpret DOM snapshots, mouse click events, and keyboard inputs to select browser actions.
  6. A context composer that feeds relevant info into LLM prompts based on user interactions, page content, and memory.