24-MAG

Software Engineer

• Embed within enterprise AI workflows as a technical research collaborator
• Work alongside domain experts and enterprise teams to understand real-world system behaviour
• Identify, formalise, and prioritise failure modes emerging from deployed AI systems
• Translate operational issues into structured research questions and measurable technical problems
• Produce analyses of system behaviour, limitations, and opportunities for improvement
• Design high-signal datasets targeting model and system weaknesses
• Develop evaluation protocols, quality criteria, and structured assessment frameworks
• Identify gaps in existing datasets and evaluation coverage
• Run rapid experimental cycles to test hypotheses and quantify system improvements
• Develop and benchmark agentic workflows for robustness, reliability, and scalability
• Evaluate AI systems operating across complex enterprise workflows
• Analyse experimental results and determine whether improvements are meaningful and reproducible
• Iterate on datasets, evaluations, and system configurations based on research findings
• Build lightweight tooling for evaluation, data curation, experimentation, and rapid iteration
• Collaborate across research, engineering, product, domain, and enterprise-facing teams
• Translate research findings into clear, decision-oriented recommendations
• Contribute to reports, benchmarks, evaluation documentation, and technical analyses
• Communicate complex findings to technical and non-technical stakeholders

Member of Technical Staff, Enterprise AI

Bilingual

• Analyse and evaluate AI-generated psychological content for clinical accuracy, relevance, and ethical appropriateness
• Review psychological case studies, assessments, research scenarios, and therapeutic dialogues
• Identify clinically significant inaccuracies, unsupported reasoning, inappropriate recommendations, bias, and culturally inappropriate assumptions
• Assess whether generated content reflects sound psychological principles and professional standards
• Develop and review datasets involving psychological case studies, assessments, conversations, and related clinical material
• Evaluate realistic psychological and psychiatric contexts and contribute high-quality examples for AI training and evaluation workflows
• Review narrative content for consistency, clinical plausibility, and appropriate psychological reasoning
• Recommend evidence-based improvements to strengthen clinical and psychological quality
• Apply ethical, cultural, linguistic, and contextual considerations to sensitive mental-health content
• Produce detailed written feedback and verbal insights explaining evaluation findings
• Communicate complex psychological concepts in accessible and professionally precise language
• Collaborate with interdisciplinary teams to integrate psychological frameworks into AI training processes
• Contribute to documentation, evaluation standards, and knowledge-sharing resources
• Help maintain consistency and clinical rigour across ongoing AI development workflows

Bilingual Mental Health Expert

Engineer

• Work directly with AI research teams and enterprise partners to define research goals, technical requirements, and project direction
• Translate ambiguous AI and machine-learning problems into clearly scoped technical projects
• Act as a technical implementation partner across research, engineering, product, and stakeholder teams
• Move between research questions, technical architecture, hands-on engineering, and partner-facing execution
• Own technical systems from discovery through implementation, deployment, iteration, and ongoing reliability
• Build large-scale data-intelligence systems for collecting, organising, evaluating, and improving training and evaluation data
• Implement ML pipelines for data curation, model training, evaluation, experimentation, and continuous improvement
• Develop infrastructure for model inference, experimentation, evaluation, and deployment
• Design reliable workflows supporting advanced AI research and production environments
• Build and maintain systems supporting complex data and machine-learning workloads
• Develop LLM applications including multi-agent systems, tool-using agents, RAG workflows, and human-in-the-loop systems
• Build evaluation harnesses and infrastructure for assessing agent and model behaviour
• Develop systems that extend AI experiments into reliable, repeatable, multi-turn workflows
• Implement agentic automation across technically complex business and research processes
• Apply modern LLM tooling and model-evaluation approaches to production-oriented AI systems
• Design data taxonomies, labelling systems, evaluation rubrics, and quality frameworks
• Improve dataset structure and quality to support stronger model performance and research outcomes
• Develop workflows for dataset curation, annotation, validation, and quality assurance
• Analyse data and model behaviour to identify opportunities for system improvement
• Apply rigorous standards to training data, evaluation datasets, and research workflows
• Project scope and research priorities may evolve depending on technical and partner requirements

Forward Deployed Engineer

Research Engineer

• Analyse, debug, and resolve technical issues across diverse software codebases
• Investigate bugs, implementation failures, incorrect behaviour, and edge cases
• Apply algorithmic reasoning and data-structure knowledge to complex coding problems
• Evaluate solution paths and select appropriate technical approaches
• Design and implement new features and technical enhancements
• Refactor codebases to improve maintainability, clarity, and adaptability
• Optimise code for performance, efficiency, and reliability
• Review implementation decisions and identify architectural or technical improvements
• Produce high-quality, well-structured code that can be tested and evaluated consistently
• Develop and review realistic coding tasks used to evaluate AI model capabilities
• Assess AI-generated code for correctness, completeness, performance, and technical quality
• Identify logic errors, implementation weaknesses, constraint violations, and incomplete solutions
• Validate generated outputs against expected behaviour and objective technical requirements
• Author feedback and annotations supporting AI model training and evaluation
• Document technical decisions, best practices, implementation details, and problem-solving approaches
• Collaborate with technical stakeholders and open-source contributors
• Communicate complex engineering concepts clearly in written and verbal form
• Contribute to knowledge-sharing and consistent technical standards

Research Engineer – Code Generation, Model Evaluation

• Architect self-contained reinforcement-learning environments for complex real-world tasks
• Design reward functions, verifiers, evaluation logic, and supporting environment components
• Structure environments for reliable experimentation and measurable model improvement
• Translate research objectives into rigorous RL workflows
• Ensure environments are reproducible, testable, and suitable for iterative model development
• Design and scale episode pipelines and multi-component training processes
• Build reproducible experimentation workflows for reinforcement-learning research
• Develop systems for running, tracking, and analysing large-scale training experiments
• Improve reliability and efficiency across RL training infrastructure
• Build automated synthetic data-generation systems
• Develop AI-driven evaluation and quality-assurance systems for grading, validation, and feedback
• Establish automated feedback loops to improve training-data and model quality
• Design verification systems that distinguish strong model behaviour from superficially plausible outputs
• Fine-tune and optimise open-source reinforcement-learning and machine-learning models
• Develop benchmarking frameworks measuring capability, robustness, and data quality
• Analyse model behaviour across internal and external evaluation environments
• Contribute to the development, release, and interpretation of research evaluations and benchmark results
• Operate across research experimentation and production-oriented technical implementation
• Adapt research priorities, evaluation systems, and experimentation workflows as project requirements evolve

Member of Technical Staff, Research Engineering

Financial Planning and Analysis

• Build, update, review, and troubleshoot detailed Excel-based financial and transaction models
• Apply valuation methodologies across acquisition, sale, and strategic transaction scenarios
• Analyse assumptions, sensitivities, and accounting treatments and their impact on model outputs
• Review financial models for methodological accuracy, logical consistency, and numerical integrity
• Identify and correct errors affecting transaction analysis or valuation conclusions
• Conduct company and industry research for transaction and strategic analysis
• Perform buyer screening, target screening, comparable company analysis, and related research workflows
• Evaluate industry dynamics, company positioning, transaction context, and market information
• Connect research findings to valuation, transaction rationale, and strategic conclusions
• Perform and review valuation analyses, including comparable company analysis
• Evaluate valuation adjustments, transaction sensitivities, and accounting considerations
• Review transaction materials for numerical inaccuracies, weak logic, or unsupported conclusions
• Provide clear, actionable feedback to improve analytical quality and consistency
• Prepare and edit investment banking presentations, pitchbooks, and client deliverables
• Structure transaction narratives and strategic rationale clearly and professionally
• Review materials against senior-banker and client-quality standards
• Communicate analytical conclusions accurately and explain complex financial concepts to specialist and broader professional audiences
• Apply investment banking expertise to AI training and financial analysis workflows

Investment Banking Expert

Artificial Intelligence

• Submit 30 high-quality, unedited selfies covering the past five years
• Submit 10 video recordings of yourself from the same period
• Ensure submitted materials clearly show the participant and meet project quality requirements
• Submit materials captured with a smartphone, camera, or comparable personal device
• Review photographs and videos for clarity and suitability before submission
• Remove or avoid filters, visual effects, and edits that may alter facial appearance
• Follow project specifications for file quality, content, and presentation
• Correct submission issues in response to feedback
• Follow project instructions throughout the data collection process
• Respond to quality feedback or requests for correction
• Report technical, quality, or submission issues
• Coordinate with the participating twin to ensure both individual applications are completed

AI Facial Data Collection Associate, Twins

• Own research and evaluation initiatives from problem framing through data design, quality calibration, and signal validation
• Define rigorous approaches for determining whether experimental results provide reliable and defensible research signal
• Analyse model and system failures to identify root causes, edge cases, and opportunities for improvement
• Evaluate datasets, experiments, and conclusions against appropriate quality thresholds
• Act as a quality gate when signal strength, data integrity, or supporting evidence is insufficient
• Design ML-oriented data systems, including task definitions, annotation schemas, rubrics, incentives, and supporting pipelines
• Structure data and evaluation workflows around downstream model-performance objectives
• Translate ambiguous real-world behaviour into measurable evaluation frameworks and new data categories
• Identify evaluation or dataset coverage gaps and recommend additional investment or iteration
• Develop quality-assurance processes that maintain consistent research standards
• Investigate model and system behaviour to identify recurring weaknesses and performance limitations
• Iterate on evaluations, datasets, feedback loops, and quality standards
• Use experimental findings to guide improvements in model or agent performance
• Determine when research directions should be expanded, revised, paused, or discontinued based on evidence
• Maintain a systems-level perspective focused on end-to-end AI performance
• Collaborate with researchers, domain experts, operators, and cross-functional teams during project kickoff, calibration, and iteration
• Communicate research findings, trade-offs, limitations, and signal strength to technical and non-technical stakeholders
• Translate research progress into evidence-grounded narratives
• Support alignment between experimental work and real-world system requirements
• Contribute technical judgement in ambiguous, high-impact research environments

Member of Technical Staff, Frontier AI

• Design and own evaluation frameworks for advanced coding agents
• Develop benchmark specifications, scoring methodologies, rubrics, and quality standards
• Establish rigorous methods for measuring coding-model performance across diverse software-engineering tasks
• Define objective criteria for correctness, reasoning quality, robustness, and task completion
• Maintain methodological rigour, reproducibility, and consistency across evaluation workflows
• Develop high-quality datasets, golden examples, and structured evaluation protocols
• Design technical tasks for reliable assessment of frontier coding systems
• Build data and evaluation workflows supporting model development and iterative improvement
• Identify benchmark or dataset coverage gaps and develop new evaluation categories
• Analyse coding-agent behaviour and identify systematic weaknesses, failure modes, and performance limitations
• Investigate incorrect reasoning, implementation errors, tool-use failures, and incomplete task execution
• Translate findings into recommendations for model training and evaluation
• Design experiments testing hypotheses about coding-model capabilities
• Build tooling and infrastructure for large-scale experimentation, data generation, review workflows, and evaluation pipelines
• Automate technical processes to improve evaluation efficiency and research velocity
• Collaborate with researchers, engineers, and applied AI teams
• Contribute to technical reports, benchmark studies, research documentation, and external-facing research initiatives
• Communicate complex technical findings to specialist and broader technical audiences

Member of Technical Staff, Coding Research

Revenue Operations

• Audit CRM and integrated revenue systems for contradictions, obsolete policies, duplicate records, and operational inconsistencies
• Review CRM configuration across fields, flows, validation rules, objects, permissions, and related administrative components
• Identify where system behaviour no longer reflects documented or intended business processes
• Analyse duplicate entities, outdated automation, incorrect permissions, and other sources of operational drift
• Analyse conflicting business rules and determine which policies or operational logic should take precedence
• Document clear reasoning behind recommended system and policy decisions
• Navigate ambiguous or undocumented operational scenarios and support consistent handling of exceptions and cross-system dependencies
• Assess and reconcile data across CRM and connected revenue platforms
• Evaluate customer, account, entitlement, billing, and related operational information across systems
• Evaluate impacts across billing, subscription management, CPQ, ticketing, project tracking, and integration platforms
• Identify discrepancies affecting reporting, workflow reliability, or operational accuracy
• Draft operational prompts and guidance for complex SaaS data and system scenarios
• Document recommended changes and areas intentionally left unchanged
• Explain technical and operational reasoning to technical and non-technical stakeholders
• Contribute recommendations for CRM design, optimisation, and sustainable governance
• Help establish transparent and reproducible decision-making across CRM and revenue-system workflows

Salesforce, HubSpot Administrator – Revenue Systems

General

• Review short video and audio clips across a variety of content types
• Analyse visual and auditory elements for detailed annotation and assessment
• Apply project-specific labels and evaluation criteria consistently
• Identify events, objects, actions, speech, sounds, and other content features
• Maintain high annotation accuracy across repetitive, high-volume tasks
• Evaluate AI-generated descriptions against underlying video and audio content
• Verify generated outputs accurately reflect source material
• Identify factual errors, omissions, inconsistencies, and ambiguous descriptions
• Assess outputs according to project guidelines and quality standards
• Flag content requiring clarification or failing requirements
• Provide clear written feedback on inaccuracies, uncertainties, and annotation issues
• Explain evaluation decisions when additional reasoning is required
• Support quality-assurance processes to improve annotation reliability
• Follow annotation protocols, project instructions, and data-handling requirements
• Collaborate remotely with project coordinators and other contributors
• Adapt to new annotation tools, guidelines, and evolving quality standards
• Identify recurring workflow or annotation issues and suggest practical improvements
• Maintain data confidentiality, accuracy, and integrity throughout the engagement

Video Annotation Specialist

• Analyse and contribute to case-based scenarios reflecting the complexities of child and youth abuse victimisation
• Apply trauma-informed clinical frameworks when reviewing sensitive mental health scenarios
• Assess psychological, behavioural, and contextual factors relevant to young abuse survivors
• Identify clinical nuances, risks, and safeguarding considerations within case materials
• Review and assess model-generated mental health guidance related to youth abuse cases
• Identify clinical inaccuracies, ethical concerns, oversimplifications, or inappropriate recommendations
• Evaluate whether generated responses reflect appropriate trauma-informed principles
• Provide expert feedback on content accuracy, safety, sensitivity, and relevance
• Develop and curate content outlining best practices for supporting young abuse survivors
• Contribute clinical guidance reflecting current trauma-informed approaches and standards of care
• Structure high-quality examples and reference materials for AI training workflows
• Document recommendations to improve system sensitivity and clinical usefulness
• Collaborate with cross-disciplinary teams to ensure project deliverables reflect current mental health standards
• Participate in iterative review cycles to refine training data and improve content quality
• Identify recurring gaps or weaknesses across AI-generated responses
• Maintain rigorous standards around privacy, confidentiality, ethics, and professional judgement

Child & Youth Abuse Trauma Specialist

• Analyse CRM, billing, ticketing, and related operational data across SaaS revenue workflows
• Identify misaligned system behaviour, operational logic, and business policies
• Review interconnected systems for inconsistencies, outdated configurations, and process gaps
• Assess downstream effects of changes across related platforms
• Surface and document conflicting policies, outdated automations, duplicate entities, and permissions mismatches
• Evaluate ambiguous processes and competing rules for complex scenarios
• Document exceptions and edge cases requiring additional judgement
• Evaluate the desired state of operational and revenue data
• Identify discrepancies, inconsistencies, and logical conflicts across connected systems
• Define corrections while preserving data integrity and operational continuity
• Assess cascading impacts before recommending or applying changes
• Draft operational prompts, recommendations, and handling instructions
• Record reasoning behind proposed changes, including intentionally unchanged areas
• Produce detailed documentation for technical and non-technical stakeholders
• Explain operational trade-offs, policy interpretations, and system dependencies
• Help establish transparent and reproducible decision-making across revenue operations workflows

Revenue Operations & CRM Systems Specialist – SaaS

• Analyse complex multi-part mechanical assemblies provided in native FreeCAD format
• Interpret component relationships, interfaces, geometry, and overall assembly design intent
• Review engineering drawings and supporting technical information
• Identify key mechanical functions, design considerations, and dimensional relationships
• Apply sound engineering judgement when evaluating assembly behaviour and technical details
• Develop objective, precise, and non-guessable question-and-answer sets for assigned assemblies
• Create detailed technical questions covering mechanical functions, design rationale, and dimensional considerations
• Produce accurate answers supported by source assembly data
• Verify technical conclusions against CAD geometry, drawings, and component relationships
• Review question-and-answer pairs for logical consistency and technical correctness
• Prepare clear technical documentation and supporting notes
• Communicate with project coordinators regarding questions, ambiguities, or technical issues
• Incorporate feedback and adapt deliverables to evolving project standards
• Manage assigned work independently while maintaining agreed timelines and quality expectations