AI-Native Engineer

The Company

Mirror is an NYC-based startup building the AI stack for modern drug discovery. We develop agents that give scientists the leverage to explore, test, and advance new medicines faster and at lower cost. Our platform focuses on bridging the gap between frontier model capabilities and the practical challenges faced by biologists and chemists across the preclinical pipeline to dramatically accelerate scientists’ daily work, without sacrificing transparency, control, or data security. By compounding advancements in agent performance, efficiency, and reliability, we’re paving the way toward transforming therapeutics development and unlocking a new era of human health.

What We Offer

• Competitive salary + meaningful equity

• Full health, dental, and vision benefits for you and your family

• Personal fitness budget

• Unlimited PTO and all national holidays

Location & Work Model

We expect you to get things done, whatever it takes. We’re typically in-office. Visa sponsorship is available.

Equal Opportunity

Mirror is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for our employees.

The Opportunity

We’re an AI-native company. Achieving efficiency and scale by leveraging AI is both what our customers pay us for and a priority for every employee.

As such, nearly everything we build is an AI system, not a one-off solution. We need full-stack engineers with a deep understanding of traditional software development as well as a strong command of how to use and deploy the modern AI stack.

Drug discovery is a rich field driven by experts with deep knowledge, experience, and taste. As a lead engineer, you’ll help us build the highest-quality platform for these experts, as well as the surrounding systems that enable scale without sacrificing rigor or reliability.

Why would I want this job?

There are remarkably few opportunities for software engineers to work on interesting problems that tangibly impact a meaningful mission of significant scale. We’re building solutions for scientists at the front lines of translating capital into therapeutics—not as a research project, but with all of the practical constraints involved in bringing drugs to the clinic (i.e., to humans whose lives they might improve). When we succeed, it’s because we’ve created value for those scientists and substantially advanced their efforts.

What You’ll Do

• Design, architect, and ship production systems across the stack, including distributed compute, databases and data pipelines, backend services, user-facing applications, and observability infrastructure.

• Build and improve multi-agent systems, agent harnesses, tool integrations, and inference infrastructure.

• Develop internal and external benchmarks and evaluations that measure scientific quality, reliability, and usefulness.

• Measure and improve agent quality, cost, latency, and robustness.

• Build repeatable pipelines for integrating and verifying new tools, with the traceability required for scientific work.

• Create feedback loops that turn evaluation results and real-world usage into systematic agent improvements and keep benchmark sets current.

• Ship new harness, interface, and enterprise capabilities, and automate high-leverage internal workflows as company priorities evolve.

• Help define Mirror’s technical architecture, engineering standards, and hiring bar as the team grows.

What We’re Looking For

• Exceptional software engineering and system design skills, with the range to work effectively across multiple layers of the stack.

• A record of owning complex projects end to end: prototyping quickly, making sound architectural decisions, and turning successful prototypes into reliable production systems.

• Practical fluency in building, deploying, evaluating, and operating modern AI systems.

• Strong product judgment and the ability to translate ambiguous user needs into simple, effective systems.

• Clear thinking and excellent written and verbal communication.

• Deep curiosity, intellectual honesty, urgency, and a consistently high bar for quality.

Relevant backgrounds may include working as a founding or early engineer, building production AI systems at a startup, or developing complex systems at a larger technology company. We care more about evidence of exceptional ability, ownership, and learning velocity than a particular credential or company name.