Backend Software Engineer: Python — Mercor (Remote, Contract)

Mercor is looking for experienced backend engineers to design, validate, and maintain real-world coding benchmarks used by leading AI labs. This is a remote, part-time contract for skilled Python developers who value clean tests, reproducible code, and concise technical writing. The role starts immediately and runs for about one month with significant bonus potential.

Why this role matters

Benchmarking shapes how AI models are evaluated. As a Backend Software Engineer (Python) at Mercor you will help create reliable, real-world tasks and test suites that improve the quality and safety of machine learning systems. Your work will be used to evaluate and fine-tune powerful models, making a tangible impact on AI development.

What you’ll do (Key Responsibilities)

• Curate coding issues, canonical solutions, and test suites from real repositories to create benchmark tasks.
• Author comprehensive unit and integration tests to verify solutions reliably.
• Debug, optimize, and document benchmark code to ensure reproducibility and clarity.
• Standardize task formatting and distribution at scale for consistent evaluation.
• Provide structured, actionable feedback on solution quality, readability, and edge cases.

Who we’re looking for (Ideal Qualifications)

• 3–10 years in backend engineering, ML engineering, or applied data science.
• Degree in Computer Science, Software Engineering, or related field (or equivalent experience).
• Strong proficiency in Python; comfortable with testing frameworks and debugging tools.
• Experience writing clear technical documentation and reproducible tests.
• Attention to detail and ability to work asynchronously with minimal supervision.

Project timeline & compensation

Start: Immediate.
Duration: ~1 month (part-time, 15–20 hrs/week).
Compensation: $80/hr base, with lucrative per-task bonuses. Median inclusive pay reported around $200/hr. Payments sent daily via Stripe Connect. Independent contractor agreement.

How applications work

1. Upload an up-to-date resume highlighting Python test development, debugging, and code review experience.
2. Complete a 15-minute conversational AI interview to discuss background and motivations.
3. Take a brief practical assessment that tests real-world coding and debugging skills.
4. Receive follow-up and onboarding details within a few days if shortlisted.

Apply here: Mercor — Backend Software Engineer (Python)

Interview Preparation Guide

Role-specific questions (8–12)

• Describe a time you designed tests for a complex Python module. How did you ensure coverage?
• How do you approach creating reproducible integration tests for code that depends on external data?
• Explain a debugging workflow you used to find a hard-to-reproduce bug.
• What patterns do you use to write readable, maintainable benchmark tasks?
• How do you measure the reliability of a test suite?
• Walk us through how you would convert a messy repository example into a clean benchmark task.
• How do you document edge cases and failure modes for reviewers?
• Give an example where you optimized a test or code to reduce flakiness.

General interview questions (5–7)

• Why are you interested in benchmark engineering for AI systems?
• How do you organize your day when working asynchronously?
• Describe a project where you collaborated with non-technical stakeholders.
• What tools do you use for version control and code review?
• How do you handle feedback on your code or documentation?

Suggested answers — talking points

• Testing: emphasize test-driven design, use of pytest/unittest, fixtures, mock objects, and continuous integration for consistency.
• Reproducibility: cite deterministic seeds, containerization (Docker), pinned dependencies, and sample datasets with checksums.
• Debugging: outline steps — reproduce, isolate, add logging, bisect changes, write regression tests, fix and review.
• Documentation: include clear README, example runs, expected outputs, and error handling notes; attach minimal reproduction cases.
• Async work: demonstrate proactive updates in issue trackers, concise PR descriptions, and clear prioritization of tasks.

Do’s & Don’ts

Do’s

• Do include clear, minimal reproductions with every benchmark task.
• Do write tests that fail on incorrect solutions and pass on correct ones.
• Do use descriptive commit messages and PR templates.
• Do check platform compatibility and dependency pinning.
• Do explain edge cases and rationale in the task description.
• Do suggest alternative inputs and expected outcomes for reviewers.

Don’ts

• Don’t submit flaky tests with time-dependent behavior.
• Don’t rely on private or proprietary datasets without providing a safe public alternative.
• Don’t skip documentation for installation or running tests.
• Don’t ignore code style or inconsistent naming conventions.
• Don’t submit overly large tasks that take long to run without sample runs.
• Don’t conceal assumptions about environment or dependencies.

Preparation checklist

• Resume updated with relevant projects and links to GitHub repos.
• Sample benchmark tasks or PRs ready to show during the interview.
• Environment: Python 3.8+ installed, virtualenv/venv, pytest, Docker (if used).
• Stable internet, quiet space, webcam and microphone tested for the AI interview.
• Notes on key projects, metrics improved, and specific challenges solved.
• If possible, prepare a short 2–3 minute walkthrough of a benchmark you wrote.

Extra pro tips

• Share concise reproducible examples on GitHub and include a README with quick start commands.
• Prioritize clarity over cleverness in tests — easy-to-read checks help reviewers move faster.
• Use small, focused commits so reviewers can follow your thought process.
• When asked about failures, describe lessons learned and how you prevented recurrence.
• Practice a short elevator pitch on why benchmark quality matters for AGI safety and robustness.

Embedded Templates (Previews)

Sample CV / Resume

Sample Cover Letter

Sample Motivation Letter / Statement

Sample Application Email

Tips to Work With This Role

1. Overview

This contract requires converting repository examples into benchmark tasks and robust tests. Core responsibilities include task curation, test creation, debugging, documentation, and feedback for reviewers. The role is essential because high-quality benchmarks increase model robustness and developer trust.

2. Step-by-step process (workflow)

Workflow (text flowchart):

Identify candidate repo example → Extract minimal reproducible case → Create canonical solution → Write unit & integration tests → Run CI locally → Document usage and edge cases → Submit PR / task package → Respond to review feedback

Tools, documents & platforms

• Tools: Python, pytest, Docker, git, GitHub, CI (GitHub Actions), VS Code
• Docs: README, CONTRIBUTING, test fixtures, sample datasets (with checksums)
• Platforms: Mercor task portal, Stripe Connect for payments

3. Illustrative example — Problem vs Solution

Problem:

A repository contains a slow, flaky example that relies on external network calls and inconsistent inputs.

Solution:

Extract minimal input, add deterministic fixtures, mock external calls, write unit tests asserting output for edge cases, include a small dataset and expected output files. Document run commands and expected runtime (under 10s).

4. Learning & Resources

Suggested upskilling paths

• Free/intro: Coursera — Python for Everybody (free audit)
• Professional certificate: Pluralsight / Udacity — Backend or Testing Nanodegree
• Advanced: University certificate in Software Engineering (e.g., Coursera partner course or local university program)

5. Example deliverable (mini)

Deliver a zipped task package containing: minimal code, solution file, tests (pytest), README with run instructions, expected outputs, and a small dataset (or mock generator).

Bonus: How to make your submission stand out

• Include CI status badges in your sample repo and passing test logs.
• Provide a short demo script that runs the tests in under a minute.
• Attach a short changelog and rationale for design decisions.
• Use clear naming and modular functions for easy review.

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