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Associate Software Engineer, RLE

Handshake

Associate Software Engineer, RLE

Handshake

San Francisco, CA

·

On-site

·

Full-time

·

Today

About Handshake

Handshake is the career network for the AI economy. 20 million knowledge workers, 1,600 educational institutions, 1 million employers (including 100% of the Fortune 50), and every foundational AI lab trust Handshake to power career discovery, hiring, and upskilling, from freelance AI training gigs to first internships to full-time careers and beyond. This unique value is leading to unparalleled growth; in 2025, we tripled our ARR at scale.

Why join Handshake now:

  • Shape how every career evolves in the AI economy, at global scale, with impact your friends, family and peers can see and feel

  • Work hand-in-hand with world-class AI labs, Fortune 500 partners and the world’s top educational institutions

  • Join a team with leadership from Scale AI, Meta, xAI, Notion, Coinbase, and Palantir, among others

  • Build a massive, fast-growing business with billions in revenue

About the Role

We’re hiring an Associate Software Engineer to help build our Reinforcement Learning Environments (RLE) platform—the systems where frontier AI models learn to complete real-world work.

This is a hands-on, growth-oriented role where you’ll contribute to building core systems, learn quickly from senior engineers, and gain exposure to cutting-edge AI infrastructure.

Location:

San Francisco, CA (in-office, 5 days/week)

What You’ll Do

  • Contribute to building RLE environments and supporting infrastructure

  • Implement features across backend systems and frontend interfaces

  • Work with senior engineers to improve system reliability and performance

  • Help build modular workflow domains (e.g., engineering, finance, legal)

  • Support data pipelines that power model training and evaluation

What We’re Looking For

  • 0–2 years of experience (internships or full-time) in software engineering

  • Familiarity with backend development and APIs

  • Working knowledge of React

JS and TypeScript:

  • Understanding of databases (e.g., PostgreSQL) and basic system design

  • Eagerness to learn and operate in fast-paced environments

Nice to Have

  • Exposure to cloud platforms (AWS, GCP)

  • Experience with data pipelines or ML-adjacent systems

  • Interest in AI/ML or simulation systems

What Success Looks Like

  • Ships reliable features that improve RLE functionality

  • Grows into owning small components independently

  • Contributes to faster iteration and better developer velocity

Perks

Handshake delivers benefits that help you feel supported—and thrive at work and in life.

The below benefits are for full-time US employees.

🎯 Ownership: Equity in a fast-growing company

💰 Financial Wellness: 401(k) match, competitive compensation, financial coaching

🍼 Family Support: Paid parental leave, fertility benefits, parental coachin

💝 Wellbeing: Medical, dental, and vision, mental health support, $500 wellness stipend

📚 Growth: $2,000 learning stipend, ongoing development

💻 Office: Commuting support, free lunch, and gym in our SF office

🏝 Time Off: Flexible PTO, 15 holidays + 2 flex days

🤝 Connection: Team outings & referral bonuses

총 조회수

0

총 지원 클릭 수

0

모의 지원자 수

0

스크랩

0

Handshake 소개

Handshake

Handshake

Series E

Handshake is a career services platform that connects college students and recent graduates with employers for job opportunities and recruiting.

501-1,000

직원 수

San Francisco

본사 위치

$3.5B

기업 가치

리뷰

3.8

10개 리뷰

워라밸

3.7

보상

2.8

문화

4.1

커리어

2.5

경영진

3.2

72%

친구에게 추천

장점

Flexible work arrangements and remote options

Supportive team and environment

Good leadership and vision

단점

Limited growth and promotion opportunities

Compensation could be better

Management needs improvement

연봉 정보

7개 데이터

Mid/L4

Senior/L5

Staff/L6

Mid/L4 · Data Scientist

0개 리포트

$167,500

총 연봉

기본급

-

주식

-

보너스

-

$142,375

$192,625

면접 경험

5개 면접

난이도

3.2

/ 5

소요 기간

14-28주

합격률

60%

경험

긍정 60%

보통 0%

부정 40%

면접 과정

1

Application Review

2

Recruiter Screen

3

Technical Phone Screen

4

Onsite/Virtual Interviews

5

Final Interview

6

Offer

자주 나오는 질문

Technical Knowledge

Behavioral/STAR

Coding/Algorithm

Past Experience

Culture Fit