About this role.
This remote, project-based role at Toloka AI/Mindrift seeks experienced strategy consultants from elite firms (e.g., McKinsey, BCG, Bain) to build realistic consulting scenarios, design structured tasks for AI agents, and define evaluation frameworks. Leveraging 3+ years of hands-on client engagement experience, you will translate real-world problem-solving into learning environments that train advanced AI models. The position offers up to $60/hour and is ideally suited for analytical thinkers with strong structured problem-solving skills. Working independently, you will deliver high-quality task designs, rubrics, and golden-answer solutions that calibrate AI evaluation at scale. With a competitive annual salary of 120,000 AUD, this opportunity lets you apply your top-tier consulting expertise to cutting-edge AI development.
Role DNA
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Job Complexity
5/5Pace & Pressure
4/5Autonomy Level
5/5Communication Load
3/5Salary analysis
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Core skills
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Cover letter sample
Dear Hiring Team,
As a strategy consultant with over four years at a top-tier firm, I have refined the structured problem-solving and client delivery skills essential to this AI training role. I am excited by the opportunity to convert real engagement dynamics into rigorous learning environments that teach AI systems high-level business reasoning. My experience building financial models, conducting market analyses, and delivering client-ready recommendations under pressure aligns directly with your project requirements. I am a self-directed worker who thrives on analytical design and logical consistency, making me well-suited for this individual-contributor position. I would welcome the chance to shape the next generation of AI with Mindrift.
Sample interview questions
I would start by breaking down the client's vague problem into core hypotheses and identifying the key analytical questions needed to test them. Then I would define the scope, gather relevant data points, and structure the task as a sequence of steps: market sizing, financial analysis, and synthesis. Each step would include clear deliverables, data inputs, and decision points, mirroring a real consulting engagement.
In a due diligence project, we had conflicting financial data and incomplete market information. I prioritized the largest uncertainties, triangulated data from multiple sources, and made explicit assumptions to fill gaps. I then stress-tested the findings against scenarios and presented the recommendations with clear confidence levels, which helped the client make an informed decision.
At BCG, I often designed evaluation frameworks for internal training and for client decision processes. I defined the key criteria, created scoring rubrics with specific anchors, and calibrated them by testing on sample outputs. I would apply the same rigor to grade AI responses, ensuring consistency and alignment with business objectives.
I rely on hypothesis-driven thinking and structured frameworks to ensure that each recommendation traces back to solid evidence. I also perform sanity checks and peer reviews, and I use MECE principles to avoid overlapping or missing elements. This systematic approach maintains logical consistency throughout the analysis.
I am fascinated by how AI can augment decision-making, and I see this role as a unique way to apply my consulting toolkit to improve AI's reasoning capabilities. The chance to work on cutting-edge generative models while maintaining a flexible, project-based arrangement is very appealing. I believe my hands-on client experience can directly contribute to building high-quality training environments.
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
- Build realistic consulting project environments — create detailed project scenarios grounded in real engagement dynamics: industry context, financials, constraints, conflicting inputs, and incomplete information.
- 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.
- 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.
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