New opportunity

Software Engineer (AI Training)

Alignerr · Remote

About this role

The Software Engineer (AI Training) role at Alignerr involves building, evaluating, and improving AI-generated code to train next-generation AI systems. You will act as an expert developer providing feedback to make AI coding assistants smarter, safer, and more reliable. This is a hands-on, fully remote, and flexible contract position. Responsibilities: Write, debug, and optimize code across various languages and frameworks. Review AI-generated code for correctness, efficiency, and best practices. Identify bugs, logic errors, and security issues. Provide structured feedback on code quality. Design coding prompts and edge-case scenarios to stress-test AI models. Compare and rank AI-generated solutions. Requirements: Proficiency in one or more programming languages such as Python, JavaScript, Java, C++, Go, or TypeScript. Solid understanding of data structures, algorithms, and software design principles. Ability to reason about code with precision and communicate technical decisions clearly in writing. Self-motivated and capable of working independently. Bachelor's degree in Computer Science, Software Engineering, or equivalent practical experience. Preferred Qualifications: Experience across multiple tech stacks (full-stack, backend, systems, data engineering). Familiarity with AI/ML concepts, LLMs, or prompt engineering. Background in technical writing, code review, or mentoring. Experience with cloud platforms, DevOps, or distributed systems. Competitive programming or open-source contributions. Advanced degree in Computer Science or related fields.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

PythonJavaScriptJavaC++GoTypeScriptData StructuresAlgorithmsSoftware DesignDebuggingTechnical WritingCode ReviewPrompt EngineeringLLMs

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

High-Fidelity Code Evaluation and Debugging
The core of the role involves reviewing AI-generated code to identify logic errors, security vulnerabilities, and performance bottlenecks. You must demonstrate a systematic approach to debugging and be able to articulate why a specific implementation is suboptimal or insecure compared to industry best practices.
Structured Technical Communication
Beyond writing code, you must provide clear, structured feedback that explains the reasoning behind your technical assessments. This requires the ability to translate complex engineering decisions into precise, actionable explanations that can be used to train AI models effectively.
Prompt Design and Stress-Testing
You are expected to design challenging coding prompts and edge-case scenarios to test the limits of AI capabilities. This requires a deep understanding of software design principles to create scenarios that force the AI to handle complex, non-trivial programming problems.

Likely challenges to think through

Maintaining Objectivity in Subjective Code Review
Comparing and ranking multiple AI-generated solutions can be difficult when multiple approaches are technically valid. Prepare by establishing a consistent rubric for evaluating code based on readability, efficiency, and maintainability to ensure your rankings are defensible and systematic.
Adapting to Diverse Tech Stacks
The role requires proficiency across various languages and frameworks. You may be tasked with reviewing code in a language outside your primary expertise. Prepare by refreshing your knowledge of idiomatic patterns and common pitfalls in multiple languages like Python, JavaScript, and Go.

Practice your answers

Technical practice

Evaluating AI-Generated Code Correctness

What to coverExplain your systematic approach to reviewing code generated by an LLM. Focus on how you verify functional correctness, identify edge cases, and ensure the code adheres to language-specific best practices and security standards.

Designing Stress-Test Prompts

What to coverDescribe your methodology for creating coding prompts that effectively challenge an AI model. Discuss how you incorporate constraints, ambiguous requirements, or complex algorithmic scenarios to test the model's reasoning capabilities.

Debugging and Performance Optimization

What to coverWhen reviewing AI-generated solutions, how do you identify performance bottlenecks or logic errors? Provide examples of how you would refactor inefficient code to improve time or space complexity while maintaining readability.

Comparing Multiple AI Solutions

What to coverIf presented with two different AI-generated solutions for the same problem, what criteria do you use to rank them? Address factors such as maintainability, idiomatic usage of the language, and robustness against potential security vulnerabilities.

Applying Data Structures and Algorithms

What to coverDiscuss how you evaluate the appropriateness of chosen data structures and algorithms in AI-generated code. Suggestion: Prepare to explain trade-offs between different approaches for common problems like searching, sorting, or data manipulation.

Technical Writing for AI Feedback

What to coverThis role requires providing structured, detailed feedback. How do you explain complex technical flaws in a way that is clear, concise, and actionable for an AI training system? Focus on the importance of precision and logical structure in your written explanations.

Cross-Language Proficiency

What to coverGiven the requirement for multiple languages like Python, Go, or C++, how do you maintain proficiency across different paradigms? Suggestion: Be ready to discuss how you adapt your code review standards when switching between high-level scripting languages and lower-level systems languages.

Security and Best Practices

What to coverHow do you audit AI-generated code for common security vulnerabilities such as injection attacks or improper memory management? Discuss the importance of defensive programming and how you would suggest improvements to make the code more secure.

Behavioural practice

Working Independently and Asynchronously

What to coverDescribe a time you managed a complex project with minimal supervision. Focus on how you maintained productivity, met deadlines, and communicated your progress effectively in a remote or asynchronous environment.

Providing Constructive Feedback

What to coverShare an experience where you had to provide critical feedback on someone else's code. How did you ensure your feedback was objective, professional, and helpful for the developer's growth?

Handling Ambiguity in Requirements

What to coverDescribe a situation where you were given an ambiguous task or problem statement. How did you clarify the requirements and proceed to deliver a high-quality solution?

Maintaining Quality Under Pressure

What to coverTell me about a time you had to balance speed with code quality. How did you ensure that your output remained accurate and reliable while working under tight constraints or high-volume demands?

Sharpen your knowledge

Topics to prepare

Code Quality and Best Practices
The role requires evaluating AI-generated code for efficiency, readability, and security. Exercise: Take a piece of your own legacy code or an open-source snippet and perform a formal code review, documenting specific improvements for performance, security vulnerabilities, and adherence to clean code principles.
Prompt Engineering for Code Generation
You will be designing prompts to stress-test AI models. Exercise: Create a series of increasingly complex coding prompts that target specific edge cases, such as handling null pointers, race conditions, or memory leaks, and evaluate how different LLMs respond to these constraints.

Skills to revise

Systematic Debugging
The role involves identifying logic errors and performance bottlenecks. Exercise: Select a complex algorithm implementation and intentionally introduce subtle bugs, then practice a systematic debugging process to isolate and document the root cause and the fix.
Technical Writing for Code Explanation
You must provide structured feedback explaining technical decisions. Exercise: Write a detailed technical explanation for a non-trivial code block, focusing on explaining the 'why' behind your design choices in clear, concise, and professional language.
Comparative Code Analysis
You will be ranking AI-generated solutions. Exercise: Generate solutions for the same algorithmic problem using different coding styles or languages, then create a rubric to rank them based on time complexity, space complexity, and maintainability.

Your preparation checklist

Prepare a code review portfolio
Compile 3-5 examples of code reviews you have performed on open-source projects or peer code. Focus on identifying logic errors, security vulnerabilities, and performance bottlenecks, as these are core responsibilities for evaluating AI-generated code.
Develop a library of edge-case prompts
Create a document containing complex coding prompts that test AI models on boundary conditions, memory management, or concurrency issues. This demonstrates your ability to design scenarios that stress-test AI capabilities.
Practice structured technical feedback
Draft a template for providing feedback on code quality that balances technical correctness with readability and best practices. Ensure your writing is clear, concise, and explains the 'why' behind your suggestions.
Review fundamental data structures and algorithms
Refresh your knowledge of core computer science principles. Be prepared to explain the time and space complexity of your solutions, as you will need to evaluate the efficiency of AI-generated code.
Formulate questions for the interviewer
Prepare questions regarding the specific evaluation rubrics used for ranking AI solutions, the types of programming languages most frequently tested, and the workflow for providing asynchronous feedback.

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