Remote | Software Engineer

24-Mag•New York, NY

23h•Remote

About The Position

We are sharing a specialised consulting opportunity for experienced Software Engineers with strong open-source development backgrounds and expertise in feature implementation, bug fixing, codebase refactoring, performance optimisation, algorithms, and multi-language software engineering to contribute to an advanced AI training and software-evaluation project. Selected professionals will work with established open-source codebases, implement and refine features, diagnose complex bugs, improve legacy systems, validate code quality, and develop technically rigorous solutions across multiple programming languages. The role is particularly suited to engineers with substantive hands-on open-source contributions and strong analytical problem-solving ability. No prior experience in AI is required.

Requirements

Expert-level proficiency in at least two of the following: Python, C++, Java, Rust, TypeScript, Go, or C#
Extensive experience contributing to open-source software
Demonstrated experience with feature development
Strong bug-fixing and debugging ability
Experience refactoring established codebases
Strong performance-optimisation skills
Strong understanding of algorithms and data structures
Competitive-programming or advanced coding-problem experience is highly valuable
Ability to analyse complex constraints and multiple possible solution approaches
Strong code-review and validation skills
Excellent attention to technical detail
Strong written and verbal communication skills
Ability to produce clear technical documentation
Comfortable working independently in a remote collaborative environment
Able to deliver well-documented, high-quality code under project deadlines
No prior AI-training or model-evaluation experience is required

Responsibilities

Develop and implement new features within established open-source repositories
Extend existing systems while respecting project architecture and contribution standards
Translate functional requirements into maintainable implementations
Integrate new functionality with existing modules and dependencies
Validate changes against expected project behaviour
Analyse complex software defects
Reproduce reported issues reliably
Trace execution and data flow to identify root causes
Develop robust fixes rather than surface-level workarounds
Validate corrections across relevant edge cases and regression scenarios
Refactor legacy or difficult-to-maintain code
Improve code clarity, structure, and modularity
Reduce unnecessary complexity
Preserve expected behaviour while restructuring implementations
Align changes with established repository conventions
Identify performance bottlenecks
Analyse computational and architectural inefficiencies
Improve runtime or resource utilisation where appropriate
Compare alternative implementation strategies
Ensure optimisation work preserves correctness and maintainability
Work across codebases using Python, Java, Rust, C++, TypeScript, Go, C#, or related languages
Adapt quickly to language-specific ecosystems and tooling
Review equivalent implementations across different languages
Apply appropriate language conventions and engineering practices
Maintain technical consistency across multi-language projects
Apply advanced algorithms and data structures to challenging technical problems
Analyse multifaceted constraints
Compare multiple solution pathways
Evaluate time and space complexity
Develop efficient and technically defensible implementations
Review code for correctness and output consistency
Identify subtle implementation defects
Validate behaviour against project requirements
Assess maintainability and coding quality
Apply strong engineering standards throughout review workflows
Review proposed changes from other contributors
Evaluate implementation quality and architectural fit
Provide clear and actionable technical feedback
Collaborate remotely on issue resolution and feature development
Support consistent contribution standards across projects
Produce concise technical write-ups
Explain solution design and implementation decisions
Document important architectural or algorithmic considerations
Communicate debugging findings clearly
Maintain useful project documentation where required
Review AI-generated software solutions
Assess code for correctness, completeness, efficiency, and maintainability
Identify algorithmic or implementation weaknesses
Provide structured feedback supporting model improvement
Contribute real-world engineering judgement to AI-training workflows