Software Engineer, Backend

Mirage•New York, NY

4d•$175,000 - $300,000•Onsite

About The Position

Mirage is an AI video company building tools and infrastructure for generative media. Its products include Captions, a creative workspace for generating, editing, and designing video; Tesseract, which gives AI agents native video-editing capabilities; and APIs that let developers integrate Mirage’s models into their products. Backend engineering spans product, platform, and machine learning. The engineer will build systems, services, APIs, and ML pipelines that power the company’s product surfaces, owning critical backend systems end-to-end and working closely with product, design, client, and AI teams to deliver reliable, scalable production systems. The role is aimed at engineers who have operated at senior or staff-plus scope, although Mirage does not use hierarchical titles or levels internally. Most roles require working in person at the company’s NYC headquarters in Union Square. Benefits listed apply to full-time employees only.

Requirements

At least 5 years of professional industry experience.
A track record of shipping high-impact systems and/or products in production.
Strong problem-solving fundamentals and ability to learn.
Excellent engineering judgment combined with practicality and time-management skills.
Ability to work effectively in an extremely fast-paced environment and scope and deliver projects end-to-end.

Nice To Haves

Extensive experience working with LLMs and generative media models, with an understanding of the direction of the technology.
A grounded, collaborative approach and willingness to do what it takes to help the team succeed.
Experience as a startup founder or an early engineer at a startup.

Responsibilities

Design, build, and own backend systems end-to-end, including services, APIs, data pipelines, and infrastructure that power the products.
Solve complex technical challenges involving distributed systems, scaling, concurrency, and performance.
Integrate and operate large generative AI models in production, including deploying, serving, and scaling systems that combine internal research and external capabilities.
Instrument, experiment, and iterate in production to continuously improve system and product quality.
Design and operate core platform infrastructure, including third-party provider integrations, storage systems, security, and internal APIs.