Anyone AI

Platform Engineer

• Develop Anyone AI’s Human Data platform end to end
• Design, stand up, and harden RL, coding, STEM, and other evaluation environments
• Build reliable and repeatable environments for contributors and internal teams
• Lead platform development for project structures, pipelines, and project taxonomies
• Design pipelines from brief through tasks, quality control, and delivery
• Maintain coherent project taxonomies as domains and clients expand
• Support the operational layer covering payments/payouts, task approval/rejection, RBAC, roles management, and project revenue/cost visibility
• Set technical direction for the platform
• Prioritize environment work, pipeline development, and administrative operations
• Improve system instrumentation and operability

Senior Platform Engineer, Human Data

Full-stack Engineer

• Review ML challenges involving experiment design, model selection, datasets, metrics, preprocessing, distribution shift, contamination, label noise, feature leakage, hyperparameter tuning, train/validation/test methodology, reproducibility, and statistical significance
• Determine whether challenges are technically sound, reproducible, appropriately difficult, and require strong machine learning reasoning
• Evaluate whether datasets contain meaningful and learnable signals
• Identify unintended shortcuts or artifacts in synthetic datasets
• Determine whether tasks require genuine diagnosis of underlying ML problems rather than brute-force model selection or large hyperparameter searches
• Review evaluation metrics and improvement thresholds
• Detect metric gaming, data leakage, and evaluation flaws
• Verify reproducibility across the complete data-to-model-to-evaluation pipeline
• Assess whether challenge difficulty is appropriately calibrated
• Provide recommendations for improving, recalibrating, or excluding problematic tasks
• Analyze ML experiments, datasets, metrics, and pipelines for applied machine learning model-training and evaluation challenges

Senior Software Engineer – Open Source, SWE-Bench Evaluation

Application Engineer

• Review technical security tasks involving CVE vulnerability reproduction, exploit proof-of-concepts, vulnerability remediation, secure coding, application security testing, Docker-based vulnerability labs, exploit verification scripts, security regression testing, CVSS/CWE classification, environment and configuration analysis, and alternative attack paths
• Determine whether vulnerability environments accurately recreate original attack conditions
• Assess whether proposed fixes eliminate vulnerabilities without breaking legitimate functionality
• Review CVE reproduction environments for technical accuracy
• Determine whether vulnerabilities faithfully reproduce the original attack vector and impact
• Evaluate proposed security fixes and remediation strategies
• Review test suites to verify normal application functionality remains intact and the original exploit no longer succeeds
• Identify incomplete fixes and alternative exploitation paths
• Review Docker environments for correct software versions, services, networking, and configuration
• Detect regressions or new vulnerabilities introduced by fixes
• Recommend improvements to vulnerability reproductions, fixes, and verification logic

Application Security Engineer – CVE, Vulnerability Research

Engineer

• Review GPU and accelerator kernel implementations for correctness
• Compare outputs against reference implementations
• Evaluate numerical tolerance thresholds
• Review kernel benchmarks and determine whether comparisons are fair
• Identify performance bottlenecks and optimization opportunities
• Assess whether performance targets are realistic given hardware limits
• Review kernel translations and hardware migrations
• Identify compilation, driver, memory, shape, and runtime issues
• Determine whether technical tasks are genuinely difficult or incorrectly configured
• Provide clear, actionable technical feedback
• Implement and debug kernels
• Optimize CUDA and Triton kernels
• Translate between kernel frameworks
• Perform hardware migration and operator fusion
• Profile and benchmark performance
• Verify numerical correctness
• Debug compilation and runtime issues
• Optimize memory hierarchy and kernel-level AI workload performance

GPU Kernel Engineer – CUDA, Triton, Accelerator Performance

• Review software engineering tasks derived from real GitHub issues and pull requests
• Assess real-world bug fixes and feature implementations
• Evaluate open-source repositories, pull requests, unit tests, and test coverage
• Review repository setup and dependency management
• Assess reproducibility and environment configuration
• Evaluate task difficulty, complexity, and multi-file or cross-module code changes
• Determine whether tasks are clearly specified, technically solvable, appropriately tested, and representative of professional software engineering problems
• Identify flaky tests, missing dependencies, ambiguous requirements, version conflicts, and environment-specific behavior
• Provide clear recommendations on whether tasks should be accepted, improved, or excluded
• Provide technical feedback and quality assessments