$90–110/hr · Mercor · Full time, 40 hours a week
You build production applications that integrate frontier AI models into real software systems, identifying failure modes and feeding engineering observations back to research teams.
What you would do
- Build full-stack applications and internal tools (Python, Java, Rust, C#, C++, TypeScript) that demonstrate frontier model capabilities end-to-end
- Integrate pre-release model APIs with surrounding infrastructure - evaluation harnesses, tool interfaces, telemetry collection
- Diagnose when models fail, when APIs misbehave, or when integration assumptions are wrong, and translate these observations into clear technical feedback for research engineers
- Prototype rapidly against evolving specifications, shipping incremental working code and accepting that requirements will shift as you learn what models can do
- Maintain consistency across code quality, architecture patterns, and documentation as the team scales across multiple languages and ecosystems
Who they want
- 6+ years shipping production software at top-tier organizations, with end-to-end system ownership and demonstrated career progression
- Expert-level proficiency in Python, Java, Rust, C# and C++, with proven track record delivering code in multiple programming languages
- Hands-on ownership across the full stack: backend service and API design, modern frontend frameworks like React, data modeling, and cloud deployment
- Demonstrated ability to onboard new codebases quickly and deliver working code with minimal ramp-up time
- Strong written communication to explain complex technical decisions and system trade-offs clearly to stakeholders
Main skills
What the interview asks about
1.API integration under uncertainty
Pre-release models behave differently than documentation; engineers must adapt fast when reality diverges from specification.
For example: “You're integrating a pre-release model API where response latency is 5x expected. Walk through your debugging process and how you'd decide whether to continue or find alternatives.”
2.Multi-language architecture choices
Choosing which components belong in which language is not obvious; strong engineers optimize for maintainability and team velocity, not language loyalty.
For example: “Your evaluation harness is currently in Python. A bottleneck emerges in the metrics aggregation loop. Would you rewrite it in Rust, optimize the Python, or parallelize differently? Walk through your reasoning.”
3.Failure diagnosis and reproducibility
Model failures are often subtle and context-dependent; documentation must enable researchers to replay and understand the issue.
For example: “A model returns incoherent outputs in 2-3 percent of requests in production but never in your test suite. Design a logging and replay strategy that lets you send a reproducible case to the research team.”
4.Prototyping velocity
Early-stage work rewards speed and iterative learning over perfection; engineers must ship working code frequently under shifting goals.
For example: “You have a new requirement to add a safety layer in front of the model API. You have one week. Sketch your approach: what is the minimum viable safety check you would ship first, and what iteration would you plan for week two?”
5.Code consistency across ecosystems
Multiple languages in one project create friction; clear architecture and standards are load-bearing.
For example: “Two services are implemented in different languages; they need to agree on a contract for model output format, retry logic, and error codes. How would you ensure they stay consistent as both teams iterate?”
A task you may get
Design and implement a minimal evaluation harness that makes N inference calls to a model API, compares outputs to a baseline, and reports latency, cost, and quality metrics.
How to prepare
- Build a working REST API service and a frontend that exercises it; ensure you have hands-on experience deploying to a cloud platform
- Practice integrating with an external API (e.g., OpenAI API, a public LLM endpoint) and design a logging and error-handling strategy for an unreliable downstream service
- Study one recent GenAI application, such as an open-source project, to understand how systems handle model integration and latency challenges.
- Sketch the architecture for a system that needs to A-B test two model versions in production with per-request bucketing, logging, and failure isolation
The facts
- Pay
- $90–110/hr
- Hours
- Full time, 40 hours a week
- Where
- Remote · United States
- Open to
- USA
- Field
- Software Engineering
- Posted
- 8/3/2026
- Places left
- 10
We wrote this page from the public Mercor listing. It may be out of date, so read the full posting before you apply.