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<span>Toloka AI supports frontier model post-training by building domain-specific reinforcement learning environments, tasks, and evaluation frameworks designed by real practitioners.<br> Mindrift, powered by Toloka — a leading enterprise AI and machine learning data partner since 2014 — connects top domain experts with cutting-edge AI initiatives.<br> Backed by Toloka’s deep expertise in scalable data generation, crowd technology, and applied ML systems, Mindrift enables experts to shape how next-generation generative models learn, reason, and perform.<br> We are launching a Management Consulting domain focused on translating real-world consulting engagements into structured learning environments for advanced AI systems.<br> To do this credibly, we are assembling a team of strategy consultants from top-tier firms who can convert authentic project experience into end-to-end examples — from problem structuring and work planning to analysis, synthesis, and client-ready recommendations.<br> You will join a growing team of consultants from leading strategy firms shaping how AI learns high-level business reasoning.<br> Important: This role is exclusively for consultants with direct experience at a top-tier strategy consulting firm.<br> If you do not have hands-on project experience at one of the firms listed below, please do not apply.<br> This requirement ensures the domain is built by practitioners trained to the highest standards of structured problem-solving and client delivery.<br> Eligible firms: McKinsey & Company, Boston Consulting Group (BCG), Bain & Company, Oliver Wyman, Roland Berger, Monitor Deloitte (Deloitte S&C), EY-Parthenon, Kearney, and Strategy& (PwC).<br> Who We’re Looking For Consultants with 3+ years of experience at one of the firms listed above, with hands-on project experience in: Structuring ambiguous client problems into workable analytical plans Building financial models, market analyses, or synthesized findings from messy inputs Producing client-ready deliverables under time pressure Forming and defending recommendations under uncertainty No deep technical background is required — we will onboard you on the lightweight tools involved.<br> What You’ll Do Build realistic consulting project environments — create detailed project scenarios grounded in real engagement dynamics: industry context, financials, constraints, conflicting inputs, and incomplete information.<br> Design structured consulting tasks for AI agents — break projects into discrete tasks that mirror real consulting work: market sizing, commercial due diligence, cost optimization, growth strategy, operational diagnosis, benchmarking, and more.<br> Define evaluation criteria and quality standards — develop grading frameworks, evaluation rubrics, and golden-answer solutions for each task, used to train and calibrate an LLM-based grading system that evaluates AI outputs at scale.<br> This is a remote, project-based, individual-contributor role focused on analytical design and evaluation.<br> Skills & Requirements 3+ years at McKinsey, BCG, Bain, Oliver Wyman, Roland Berger, Monitor Deloitte, EY-Parthenon, Kearney, or Strategy& Strong structured problem-solving and hypothesis-driven thinking Ability to translate vague problems into clear analytical steps and deliverables High attention to logical consistency and output quality Independent, self-directed working style Clear written English (B2+) Compensation On this project, contributors can earn up to $60 per hour equivalent , depending on their level and pace of contribution.<br> Compensation varies across projects depending on scope, complexity, and required expertise.<br> Please note that other projects on the platform may offer different earning levels based on their requirements.<br> For this project, tasks are estimated to require around 25-30 hours per week during active phases, based on project requirements.<br> This is an estimate, not a guaranteed workload, and applies only while the project is active.<br> Tasks must be submitted by the deadline and meet the listed acceptance criteria to be accepted.<br></span> </div>