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Job Description
Toloka AI supports frontier model post-training by building domain-specific reinforcement learning environments, tasks, and evaluation frameworks designed by real practitioners.
Mindrift, powered by Toloka — a leading enterprise AI and machine learning data partner since 2014 — connects top domain experts with cutting-edge AI initiatives. 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.
We are launching a Management Consulting domain focused on translating real-world consulting engagements into structured learning environments for advanced AI systems. 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.
You will join a growing team of consultants from leading strategy firms shaping how AI learns high-level business reasoning.
Important: This role is exclusively for consultants with direct experience at a top-tier strategy consulting firm. If you do not have hands-on project experience at one of the firms listed below, please do not apply. This requirement ensures the domain is built by practitioners trained to the highest standards of structured problem-solving and client delivery.
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).
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.
What You’ll Do
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Build realistic consulting project environments — create detailed project scenarios grounded in real engagement dynamics: industry context, financials, constraints, conflicting inputs, and incomplete information.
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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.
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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.
This is a remote, project-based, individual-contributor role focused on analytical design and evaluation.
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.
Compensation varies across projects depending on scope, complexity, and required expertise. Please note that other projects on the platform may offer different earning levels based on their requirements.
For this project, tasks are estimated to require around 25-30 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active. Tasks must be submitted by the deadline and meet the listed acceptance criteria to be accepted.
Originally posted on Himalayas
