{"id":2838401,"url":"https://alion.io/job/weekday-software-engineer-open-source-contributor","title":"Software Engineer (Open Source Contributor)","company":{"id":7097,"name":"Weekday","domain":"weekday.works","url":"https://alion.io/company/weekday","size_band":"51-200","is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"B","score":80,"open_postings":113,"ghost_share":0,"stale_share":0.982,"repost_share":0,"time_to_fill_p50_days":5,"computed_at":"2026-10-21T05:46:15Z"}},"role":"Backend","role_family":"Backend","seniority":null,"employment_type":"contractor","work_mode":"remote","remote_scope":"worldwide","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"GitHub","optional":false},{"name":"Go","optional":false},{"name":"Java","optional":false},{"name":"Python","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Rust","optional":false},{"name":"TypeScript","optional":false},{"name":"CI/CD","optional":true}],"status":"live","first_seen_at":"2026-10-19T07:56:33Z","employer_posted_date":"2026-10-19","last_verified_at":"2026-10-21T19:07:42Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"This role is for one of our clients\nPay: 50-100/hour\nRole Type: Contractor\nLocation: Anywhere in the world, remote\nEstimated Time Commitment: Approximately 15 hours per task run\nStart Timeline: Within 24-48 hours of onboarding\nWe are seeking experienced Software Engineers and Open Source Contributors to help develop and evaluate next-generation AI systems through high-quality software engineering tasks. This opportunity is ideal for developers with strong programming fundamentals, practical experience contributing to open-source projects, and a proven ability to solve complex engineering problems.\nIn this role, you will design realistic, challenging, and reproducible coding environments that assess an AI model's ability to fix bugs, implement new features, refactor existing code, and optimize software performance. You will also develop reliable verification mechanisms and golden reference solutions to evaluate whether AI-generated code meets the specified requirements.\nPrior experience in artificial intelligence is not required. Your software engineering expertise, code quality standards, debugging skills, and open-source contributions will be central to the role.\nRequirements\nKey Responsibilities\nCoding Task Development: Create realistic, technically challenging programming tasks involving bug fixes, feature development, code refactoring, and performance optimization.\nReproducible Environment Setup: Build deterministic reinforcement learning environments with the necessary dependencies, test suites, configurations, and execution instructions.\nReference Solution Implementation: Develop reliable golden reference solutions that demonstrate the expected behavior and satisfy all task requirements.\nAutomated Verification: Design deterministic evaluators and test cases that accept valid implementations while rejecting incorrect, incomplete, or non-compliant solutions.\nSoftware Debugging: Investigate complex software defects, troubleshoot unexpected behavior, and resolve performance bottlenecks across diverse codebases.\nFeature Engineering: Implement robust and scalable features while following established software development principles and coding standards.\nCode Refactoring and Optimization: Improve existing codebases by enhancing readability, maintainability, execution efficiency, and long-term reliability.\nCode Review and Validation: Review code contributions and technical submissions for correctness, clarity, performance, and adherence to best practices.\nTechnical Documentation: Document problem statements, implementation decisions, debugging strategies, testing procedures, and solution reasoning to support high-quality AI training and evaluation.\nContinuous Improvement: Refine tasks and evaluation criteria based on observed model behavior and identified weaknesses.\nRequired Qualifications\nStrong software engineering experience in one or more programming languages, including Python, Java, C++, Go, Rust, or TypeScript.\nDemonstrated experience developing backend systems, scalable services, APIs, or complex software applications.\nA clear track record of open-source contributions, preferably supported by a GitHub profile or equivalent public portfolio.\nStrong understanding of data structures, algorithms, software design principles, and debugging techniques.\nPractical experience with automated testing, code validation, and software quality assurance.\nAbility to identify and resolve complex bugs, implementation errors, and performance bottlenecks.\nExperience developing new features and improving existing codebases.\nStrong analytical thinking, problem-solving, documentation, and technical communication skills.\nAbility to work independently, follow detailed technical specifications, and deliver reproducible, verifiable solutions.\nInterest in AI systems and understanding how software engineering expertise can support AI model training and evaluation.\nMust-Have Skills\nSoftware Engineering\nOpen Source Contributions\nBackend Development and API Design\nPython, Java, C++, Go, Rust, or TypeScript\nDebugging and Troubleshooting\nData Structures and Algorithms\nAutomated Testing and Code Validation\nCode Review and Refactoring\nPerformance Optimization\nGood-to-Have Skills\nExperience with large-scale or legacy codebases\nReinforcement Learning Environment Development\nDeterministic Testing and Automated Evaluation\nCI/CD and Development Tooling\nScalable Distributed Systems\nPerformance Profiling and Optimization\nComplex Feature Implementation\nAI Model Evaluation and Coding Agent Assessment\nPublic GitHub repositories or notable open-source projects\nOpportunity Snapshot\nTime Commitment: Approximately 15 hours per task run, depending on complexity and individual workflow.\nFlexible Schedule: Work with flexibility while meeting task requirements and weekly submission minimums.\nGetting Started: Selected candidates receive onboarding instructions and the resources required to begin.\nPortfolio Requirement: A public GitHub profile or another portfolio demonstrating relevant open-source contributions is preferred.\nAlternative Assessment: Candidates whose public portfolios do not sufficiently demonstrate their skills may be required to complete a HackerRank technical assessment.\nCompensation: Payment is based on completed tasks that meet the specified quality and acceptance criteria.\nSelection Process\nApplication: Submit your application and complete the initial screening questions.\nAI Interview: Complete an approximately 20-minute interview focused on your technical experience and problem-solving approach.\nTechnical Assessment: Complete a HackerRank assessment if your portfolio does not adequately demonstrate the required expertise.\nHiring Manager Review: Your application, technical capabilities, and assessment results will be reviewed.\n Onboarding: Selected candidates receive onboarding materials and instructions for beginning their assignments.\nCandidates are expected to complete interviews and assessments independently so their technical judgment and problem-solving abilities can be evaluated accurately.\nExpected Availability\nThe selection process may move quickly, with positions often filled within approximately 48 hours. 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