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Software Engineer (new Grads)

Build and scale backend systems powering real-time AI agents for enterprise customers
San Francisco, California, United States
Entry Level
$160,000 – 250,000 USD / year
2 weeks ago
Giga ML

Giga ML

Provides tools and infrastructure for training, deploying, and scaling large language models efficiently on enterprise hardware.

Giga Engineering Role

Location: San Francisco, CA (In-office) & New York City, NY (In-office)

Employment Type: Full-time

Salary Band: Base Salary $160,000 - $250,000 (Offers Equity)

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
  • 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.

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Software Engineer (new Grads)
San Francisco, California, United States
$160,000 – 250,000 USD / year
Engineering
About Giga ML
Provides tools and infrastructure for training, deploying, and scaling large language models efficiently on enterprise hardware.