{"id":4017452,"url":"https://alion.io/job/recruitment-room-ai-trainer-ml-engineering-remote","title":"AI Trainer (ML Engineering) - Remote","company":{"id":100174,"name":"Recruitment Room","domain":"recruitmentroom.net","url":"https://alion.io/company/recruitment-room","size_band":null,"is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Education","role_family":"Education","seniority":null,"employment_type":"contractor","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"inferred_payroll_markers","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["BB"],"hiring_countries_total":1,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"GitHub","optional":false},{"name":"Hugging Face","optional":false},{"name":"JAX","optional":false},{"name":"LightGBM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"TensorFlow","optional":false},{"name":"XGBoost","optional":false},{"name":"AI Agents","optional":true}],"status":"live","first_seen_at":"2026-11-10T12:30:00Z","employer_posted_date":null,"last_verified_at":"2026-11-10T12:30:00Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"AI TRAINER (ML ENGINEERING) - REMOTE\nLocation: Global, Fully Remote\nEmployment Type: Contractor\nTime Commitment: 15+ hours per week\nSchedule: Flexible\nCompensation: $60-$120/hour equivalent, paid per accepted task\nABOUT THE ROLE\nWe are seeking Machine Learning Engineers with prior professional AI-training or model-evaluation experience to support a project focused on improving advanced AI systems.\nYou will apply your machine learning expertise to develop challenging technical tasks, evaluate model-generated work, identify failures, and establish objective standards for correct solutions. This role is suited to candidates with substantive hands-on AI-training experience.\nKEY RESPONSIBILITIES\nCreate realistic machine learning engineering tasks that challenge advanced AI models.\n\nDevelop task instructions, reproducible environments, reference solutions, and evaluation criteria.\n\nEvaluate model-generated code, experiments, analyses, and technical deliverables.\n\nIdentify issues involving data leakage, incorrect validation, unstable training, performance, and reproducibility.\n\nREQUIRED QUALIFICATIONS\nPrior paid experience training, evaluating, benchmarking, or improving AI systems.\n\nStrong practical proficiency in Python.\n\nHands-on experience with PyTorch, JAX, TensorFlow, scikit-learn, XGBoost, LightGBM, or comparable machine learning tools.\n\nPUBLIC PROOF OF SKILL\nApplicants should provide verifiable evidence of their expertise, such as:\nGitHub repositories or public model implementations.\n\nKaggle, DrivenData, AIcrowd, or similar competition results.\n\nHugging Face models or datasets.\n\nResearch publications or technical reports.\n\nOpen-source machine learning contributions.\n\nPublic benchmarks, experiments, or technical projects.\n\nPREFERRED QUALIFICATIONS\nExperience creating AI-training tasks, reference answers, rubrics, graders, or benchmark problems.\n\nExperience reviewing technical submissions from other AI trainers.\n\nKaggle Expert, Master, or Grandmaster status, or strong results in other technical competitions.\n\nMachine learning publications or open-source contributions.\n\nExperience with long-horizon or agentic AI evaluations.\n\nAvailability to begin immediately.\n\nAPPLICATION PROCESS\nSubmit your application, AI-training experience, and public technical evidence.\n\nComplete an AI interview covering technical and integrity checks.\n\nUndergo a hiring-manager review.\n\nComplete project onboarding and begin your first assigned task if selected.\n\nCOMPENSATION AND WORK ARRANGEMENT\nCompensation is output-based, with an estimated equivalent of $60-$120 per hour. Payment is made for tasks that meet project specifications and quality requirements. 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