Software Engineer (New Grads)

Giga

📍New York, NY
Posted May 21, 2026

Job Overview

Position

Software Engineer (New Grads)

Company

Giga

Location

New York, NY

Work Type

On-site

Job ID

li-4374834620

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 new grad 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.

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

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

  • Are graduating (or recently graduated) with a CS degree or equivalent background

  • Have internship experience or significant projects where you wrote production-quality code

  • Can take a problem, figure out an approach, and unblock yourself when you get stuck

  • Prefer shipping over perfection but still care about quality

  • Want to work 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

  • 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 a data pipeline to automatically fetch and update time-based knowledge, like ongoing incidents from external status pages, for our AI agents?
  • 2.When building an agent memory system for conversation awareness across sessions, how would you structure the data storage and retrieval to ensure fast lookups of recent interactions?
  • 3.For our Activity Stream feature, how would you design the backend to efficiently log and filter high-volume agent behavior data with timestamps and frequency metrics?
🎯Behavioral Questions (3)
  • 1.Tell me about a time you had to ramp up quickly on a new technology or codebase to ship a feature. How did you approach the learning process?
  • 2.Describe a situation where you got stuck on a complex technical problem. How did you unblock yourself?
  • 3.Give an example of a project where you had to balance shipping quickly with maintaining code quality. How did you make trade-offs?
🧩Situational Questions (2)
  • 1.You are tasked with building a new integration for Atlas that sends alerts to Slack, but the Slack API documentation is confusing and lacks examples for your specific use case. How do you proceed?
  • 2.You just shipped a feature for the Activity Stream, but in production, you notice the frequency charts are loading very slowly for enterprise customers with high log volumes. What is your immediate action plan?

Interested in this position? Apply directly on LinkedIn.

Apply on LinkedIn →