Jobs at Weekday, Inc.

Analyze complex business problems, conduct market and competitive research, evaluate business performance, identify growth opportunities, and develop data-driven strategic recommendations. The role supports operational improvements, business transformation, and implementation initiatives through structured frameworks, financial analysis, presentations, reports, and executive-level communication. Candidates need 1–7 years of consulting experience, strong analytical abilities, excellent communication skills, and a degree from a top-ranked university.

Develop data-driven solutions to complex real-world problems using data science, machine learning, statistical modeling, and analytical reasoning. Candidates should currently work in a technical data science or machine learning role at a leading technology company, hold a quantitative undergraduate degree from a top-ranked university, and reside and work in an eligible English-speaking country.

Design, develop, test, and maintain scalable software applications and systems. Write high-quality code, solve complex technical problems, collaborate with product and engineering teams, participate in code reviews and debugging, contribute to system architecture, and improve engineering processes. The role requires strong software fundamentals, production-scale experience, and familiarity with Databricks, distributed systems, and cloud infrastructure.

Reviews and compares AI chatbot responses using predefined criteria for accuracy, relevance, completeness, clarity, consistency, and quality. Documents evaluation guidelines, analyzes workflows and data, identifies recurring issues, prepares reports, tracks quality metrics, and collaborates with project teams. This fully remote, 16-week contract requires 8 hours per day, five days per week, with Pacific Time overlap and possible weekend work.

Evaluate educational AI conversations and generated responses for accuracy, relevance, completeness, clarity, and logic. Conduct research and fact-checking, synthesize information, annotate errors, provide evidence-based feedback, and create training data examples to improve AI models. The role requires reviewing genuine Gemini conversation history, submitting an exported JSON file, and working full-time remotely for a 16-week contract with PST overlap.

Design and validate complex engineering simulation problems for training and evaluating AI models and coding agents. Develop Python-based simulation workflows, automated tests, reference solutions, and grading systems across electrical, mechanical, and aerospace domains. Analyze AI execution logs and failure modes, validate physical plausibility and numerical convergence, refine benchmarks, and collaborate with engineering and AI research teams. This remote contractor role requires approximately 40 hours weekly, Pacific Time overlap, and weekend on-call availability.

Evaluates AI-generated data science and quantitative reasoning outputs; develops prompts, datasets, reference solutions, and objective evaluation benchmarks. Identifies statistical errors, flawed methodologies, and unsupported conclusions while providing structured feedback to improve model accuracy. Responsibilities include dataset validation, statistical and quantitative assessment, quality assurance, technical documentation, and clear communication of analytical findings.

Develop and evaluate backend security coding tasks for AI systems. Responsibilities include implementing backend features, fixing bugs, refactoring code, optimizing performance, identifying and remediating vulnerabilities, conducting security-focused code reviews, and creating deterministic automated tests and verifiers. The role requires strong backend engineering, application security, debugging, testing, algorithms, and software architecture skills. Work is remote, flexible, part-time, and contractor-based.

Develop and debug production-grade full-stack applications across front-end and back-end systems. Build features, APIs, databases, and data models; troubleshoot end-to-end issues; analyze unfamiliar codebases; translate implementations across programming languages; and review AI-generated and developer-written code for correctness, security, scalability, performance, and maintainability.

Develop and evaluate coding tasks for AI systems, including bug fixes, feature development, refactoring, optimization, reproducible environments, reference solutions, automated testing, and deterministic evaluation. The role also involves debugging complex codebases, reviewing contributions, documenting technical decisions, and refining evaluation criteria. Candidates should have strong software engineering fundamentals, backend or API development experience, open-source contributions, and proficiency in at least one listed programming language.

Develop coding tasks and reference solutions for evaluating AI systems. Build deterministic verifiers, test suites, scalable backend services, and APIs. Debug complex defects, assess code quality, refactor legacy implementations, optimize performance, and evaluate AI-generated technical reasoning. Document requirements and verification criteria while independently applying software engineering judgment across algorithms, system design, testing, and reliability.

Build and maintain software engineering infrastructure for AI development and evaluation workflows. Develop rubrics, review and label preference data, create scalable pipelines, document findings, navigate complex codebases, and improve evaluation quality and consistency. Collaborate with researchers and engineers in an ambiguous, fast-paced environment while applying strong judgment, debugging, communication, and system-design skills.

Perform contract drafting, review, redlining, and simulated negotiations while evaluating AI-generated legal responses. Identify contractual risks, assess legal accuracy using structured rubrics, and provide actionable feedback to improve AI systems for contract review. Collaborate with legal, product, engineering, and research teams on datasets, evaluation guidelines, and quality standards.

Designs challenging data science and machine learning tasks, builds reproducible environments and verified reference solutions, and develops automated tests and evaluation frameworks for AI-generated code. The role analyzes AI agent performance, identifies bugs, leakage, overfitting, and methodological weaknesses, documents technical requirements, and iteratively improves task reliability and complexity.

Evaluates AI-generated software for correctness, quality, efficiency, security, and maintainability. Designs programming challenges, reference solutions, test cases, and evaluation criteria. Reviews technical reasoning related to debugging, architecture, and system design, then provides structured feedback and documentation. The role requires strong programming fundamentals, software testing and code review experience, analytical problem-solving ability, and clear technical communication.

Prepare and review individual U.S. income tax returns, identify complex tax edge cases, validate product outputs against professional tax software, and collaborate with engineers and founders to improve tax product functionality, roadmap, and user experience. The role requires substantial tax-season experience, strong federal tax knowledge, and expertise with professional tax preparation platforms.

Prepare and review individual U.S. income tax returns, identify complex tax scenarios and product edge cases, validate tax engine outputs against professional tax software, and collaborate with engineers on tax rules, product improvements, and user experience. Serve as a tax subject matter expert embedded within an early-stage product and engineering team.

Reviews and evaluates domain-specific documents, providing structured feedback on quality, accuracy, and relevance. The role requires strong analytical and written communication skills, detailed document review, proficiency with spreadsheet and presentation software, and the ability to follow structured guidelines. Candidates must have a bachelor’s degree, at least three years of relevant professional experience, English proficiency, and reside in the United States, Europe, the United Kingdom, or Canada.

Builds a talent network of experienced data scientists for potential future AI evaluation projects. Responsibilities may include creating grading criteria, evaluating AI-generated or human-produced data science work, writing evidence-based justifications, applying consistent judgment, and incorporating reviewer feedback. Candidates should have professional data science experience, strong Python and SQL skills, expertise in statistical modeling, machine learning, experimentation, causal inference, and excellent technical communication.

Develop and evaluate realistic Excel tasks and solutions for AI training data. Apply advanced Excel capabilities including formulas, PivotTables, data modeling, dashboards, Power Query, Power Pivot, and VBA across multiple industries and business functions. Provide written feedback, close research knowledge gaps, improve model performance, and collaborate with subject matter experts. Requires reliable weekday availability, initially 40 hours per week and 20 hours thereafter.