# Software Engineer, Virality and Activation

**Company:** [Otter](https://hotfix.jobs/companies/otter)
**Location:** Mountain View, CA
**Role:** Backend Engineering
**Salary:** $136k – $185k/yr
**Experience:** 2+ years
**Skills:** Python, Django, MySQL, DynamoDB, Cassandra, Redis, AWS, A/B Testing, Analytics
**Posted:** 2026-07-07

> Software Engineer building backend systems for user activation, onboarding, sharing, and growth features at Otter.ai. Requires 2-4 years backend experience with Python/Django, databases, and AWS; involves instrumentation, A/B testing, and cross-functional collaboration.

## Job Description

## Responsibilities
- Build and iterate on backend systems that power core activation features — onboarding, sharing, invite flows, and notification pipelines
- Instrument new features with analytics events to measure funnel conversion and user behavior, in close collaboration with data partners
- Participate in A/B experiments: support test setup, interpret results, and help translate findings into product decisions
- Write clean, well-tested, maintainable code that your teammates can build confidently on top of
- Contribute actively to design reviews, sprint planning, and team retrospectives
- Surface and address problems in existing systems — not just within your assigned scope

## Requirements
- Bachelor's degree in Computer Science or a related field, or equivalent practical experience
- 2–4 years of backend software engineering experience, with a track record of shipping user-facing features
- Working experience with Python and a backend web framework such as Django
- Familiar with relational databases (MySQL or similar) and has some exposure to NoSQL or high-performance data stores such as DynamoDB, Cassandra, or Redis — and understands when to reach for each
- Some exposure to cloud infrastructure, ideally AWS (EC2, S3, or similar services)
- Some exposure to product analytics or A/B testing, and is eager to develop deeper fluency
- Thinks about user outcomes alongside technical correctness when making engineering decisions
- Communicates clearly and works well across functions — with product, design, and data partners
- Comfortable navigating ambiguity and making progress without a fully-specified design

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