Training Turk

Software Engineer, Full Stack (Python, Java, Rust, C#, C++)

$50–65/hr · Mercor · Full time, 40 hours a week

You engineer production full-stack software across multiple programming languages that exercise cutting-edge AI models end-to-end.

What you would do

  • Develop and deploy complete applications spanning backend services, modern front-end interfaces, and database layers in your choice of language
  • Integrate experimental model APIs and build surrounding scaffolding for instrumentation, evaluation, and reproducible failure capture
  • Run integration tests to surface edge cases and model behavior anomalies, documenting findings for researchers
  • Ship prototypes and production increments rapidly as product requirements shift and evolve
  • Collaborate with engineering team on code review, architecture decisions, and technical standards maintenance

Who they want

  • 3+ years shipping production software at established tier-one organizations with hands-on systems experience
  • Proficiency in one primary language from Python, Rust, Java, C#, or C++, plus working skill in a second language
  • Proven full-stack work across backend APIs, modern front-end frameworks, relational or document databases, and cloud deployment
  • Demonstrated capability to onboard quickly into unfamiliar codebases and deliver working code with minimal ramp-up time
  • Excellent written communication skills for explaining complex technical decisions to both engineers and non-technical audiences

Main skills

Python backend developmentJava service architectureRust systems programming

What the interview asks about

  1. 1.Language selection for system constraints

    Each language trades speed, type safety, performance, and ecosystem maturity; your reasoning directly affects shipping speed and system reliability.

    For example: “Design high-throughput harness processing 100k test cases with rate-limited model API and front-end streaming. When choose Rust over Python? What onboarding friction?”

  2. 2.Rapid codebase navigation and ownership

    You're inheriting mid-flight projects with unfamiliar patterns and tight iteration expectations; slow onboarding delays delivery.

    For example: “On day one you're handed a 20k-line Java service responsible for model evaluation and telemetry. Walk me through your first four days: what patterns do you learn first, how do you ship something on Thursday, and what do you flag for the team by Friday?”

  3. 3.Failure mode diagnosis and documentation

    Model researchers need crisp, reproducible descriptions of issues you uncover; vague bug reports block their research cycles.

    For example: “Model returns different outputs for identical prompts due to trailing whitespace; affects 2% of tasks. Investigate, document, present findings to research team.”

  4. 4.API integration under incomplete documentation

    Experimental APIs shift behavior, have undocumented limits, and often lack clear error contracts; you must infer intent and fail gracefully.

    For example: “Integrate pre-release endpoint with unpredictable 503 errors and rate limits in Slack. Design wrapper: detect failures, intelligent retry, surface patterns to team.”

  5. 5.Full-stack pragmatism under time pressure

    You can't wait for perfect database schema redesigns or library updates when you ship next Tuesday; tradeoff awareness and communication matter most.

    For example: “Log 400 events per test case across 50k tests; database inserts bottlenecked. Choose: async writes, buffering, columnar, time-series. Explain decision tradeoff.”

A task you may get

Pseudo-code Python service wrapping mock LLM API with REST endpoint, validation, error handling, structured logging, and evaluation harness for metrics and failures.

How to prepare

  • Review a recent full-stack project you shipped and articulate your language choices, framework selections, and tradeoffs for each layer
  • Describe a system you joined mid-project: what confused you in week one, how you moved past gaps, and what working code you shipped by week two
  • Read release notes for 2-3 recent model API launches (OpenAI, Anthropic, Mistral) and identify patterns in documentation quality, versioning strategies, and graceful degradation
  • Prepare a real production failure you uncovered: how you isolated it, documented findings, and presented to non-technical audience

The facts

Pay
$50–65/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.