Software Engineer I / II

Giga

📍New York, NY
Posted May 21, 2026

Job Overview

Position

Software Engineer I / II

Company

Giga

Location

New York, NY

Work Type

On-site

Job ID

li-4374838577

Job Description

About Giga
Giga has recently raised a $61M Series A and has several paying customers, including DoorDash. We’re building the next generation of customer experience — real-time AI agents that can understand emotion, resolve issues instantly, and scale across the world’s largest enterprises.

It’s an exciting inflection point for the company. While we have been successful, we have larger ambitions. Our goal is to become the go-to AI platform for all enterprise automation, powered by our voice superintelligence. To achieve this, we need more great engineers.

The work affects millions of people every day and our engineers have autonomy and make true impact. This opportunity is unique because we have brilliant founders, have found commercial success, and see a clear path to becoming a generational company. Some further info about us:

  • Voice AI startup Giga raises $61M Series A

  • DoorDash and Giga Partnership

Giga builds AI agents trusted by the largest B2C companies in the world. Industry leaders like DoorDash trust Giga with their most complex support and operations workflows across voice, chat, and email. If being a part of this resonates with you, please apply!

The Role
We're looking for software engineers to help build the systems that power our AI agents. You'll work across the backend, from data pipelines and integrations to agent infrastructure, shipping features alongside experienced engineers who will help you grow.

This is a role where you'll contribute to real problems from day one. We expect you to ramp up quickly, take ownership of your work, and operate with increasing independence as you learn the codebase.

What You'll Work On
You'll contribute to projects across our stack. Some examples:

  • Atlas: Building features for our AI assistant: charts and alerts in Slack, natural language queries, and expanding Atlas to manage platform resources

  • Activity Stream: Log visualization with filters, timestamps, and frequency charts to give visibility into agent behavior

  • Dynamic knowledge: Adding time-based knowledge (like ongoing incidents) that auto-updates from sources like status pages

  • Agent memory: Conversation awareness and recent interaction lookups so agents remember context across sessions

As an early-career engineer, you'll be paired with senior engineers on larger initiatives while also owning smaller projects end-to-end as you ramp up.

You Might Be a Fit If You

  • Have 1-3 years of professional software engineering experience

  • Are curious and ask good questions—you want to understand why things work, not just how

  • Can take feedback well and iterate quickly

  • Prefer shipping over perfection but still care about quality

  • Want to grow at a startup where you'll have real responsibility early

Perks & Benefits

  • Catered lunch daily

  • Dinner stipend

  • $150/month wellness benefit (gym, fitness classes, mental health)

  • 401(k) plan

  • Paid parental leave (12 weeks maternal, 6 weeks paternal)

  • Commuter benefits

  • Medical, dental, and vision coverage

Giga is an equal opportunity employer. We're committed to providing equal employment opportunities regardless of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other characteristic protected by law.

Compensation Range: $160K - $250K

Interview Prep

AI-powered insights to help you prepare

Key Skills

Required:
Preferred:

Practice Questions

💡Technical Questions (3)
  • 1.How would you design the data pipeline to fetch and auto-update dynamic knowledge, like time-based ongoing incidents from external status pages, so our AI agents always have real-time context?
  • 2.When building an 'Activity Stream' to visualize agent behavior with filters, timestamps, and frequency charts, how would you structure the backend to handle high-volume logging without degrading agent performance?
  • 3.For the 'Agent memory' feature, how would you implement recent interaction lookups so the agent remembers context across sessions without exceeding token limits?
🎯Behavioral Questions (3)
  • 1.Tell me about a time you had to ramp up quickly on a new codebase or technology to deliver a project. How did you balance the need to learn with the pressure to ship?
  • 2.Give an example of a time you received critical feedback on your code or technical approach. How did you respond, and what was the outcome?
  • 3.Describe a situation where you had to make a trade-off between shipping a feature quickly and achieving technical perfection. How did you decide?
🧩Situational Questions (2)
  • 1.You are tasked with building a new feature for Atlas to send alerts to Slack, but the API documentation for the third-party integration you need is outdated and confusing. Your senior engineer is busy in a meeting. What do you do?
  • 2.You just shipped a small project end-to-end, but in production, you notice the agent's response time has increased by 20% due to the new feature. How do you handle this?

Resume Keywords

Make sure these keywords appear on your resume

BackendData PipelinesIntegrationsAI AgentsPythonLog VisualizationReal-timeShippingAutonomyAPI

Interested in this position? Apply directly on LinkedIn.

Apply on LinkedIn →