Please submit your CV in English and indicate your level of English proficiency. Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment. What this opportunity involves While each project involves unique tasks, contributors may:  - Design original computational physics problems that simulate real physics research workflows; - Create problems requiring Python programming to solve (using Numpy, SciPy, Sympy); - Ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks); - Develop problems requiring non-trivial reasoning chains in mechanics, electromagnetism, thermodynamics, and quantum mechanics; - Base problems on real research challenges or practical applications from physics practice; - Verify solutions using Python with standard physics simulation libraries; - Document problem statements clearly and provide verified correct answers. What we look for This opportunity is a good fit for physicians with an experience in python open to part-time, non-permanent projects. Ideally, contributors will have:  - Degree in Physics (Theoretical, Experimental, or Computational) or related fields; - Python proficiency for numerical validation. MATLAB, R, C, SQL, Numpy, Pandas, SciPy, domain-specific libraries, Stata or knowledge of any programming language can be equivalent; - 2+ years of professional experience: applied, research, or teaching experience is applicable; - Experience with numerical simulation methods; - Ability to design problems that mirror real physics research workflows; - Creative thinking in problem design across diverse physics areas; - Familiarity with physics modeling and approximation techniques; - Strong written English (C1+). How it works Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid Project time expectations For this project, tasks are estimated to require around 10–20 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. Compensation On this project, contributors can earn up to $76 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.

Please submit your CV in English and indicate your level of English proficiency. Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment. What this opportunity involves While each project involves unique tasks, contributors may:  - Design original computational physics problems that simulate real physics research workflows; - Create problems requiring Python programming to solve (using Numpy, SciPy, Sympy); - Ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks); - Develop problems requiring non-trivial reasoning chains in mechanics, electromagnetism, thermodynamics, and quantum mechanics; - Base problems on real research challenges or practical applications from physics practice; - Verify solutions using Python with standard physics simulation libraries; - Document problem statements clearly and provide verified correct answers. What we look for This opportunity is a good fit for physicians with an experience in python open to part-time, non-permanent projects. Ideally, contributors will have:  - Degree in Physics (Theoretical, Experimental, or Computational) or related fields; - Python proficiency for numerical validation. MATLAB, R, C, SQL, Numpy, Pandas, SciPy, domain-specific libraries, Stata or knowledge of any programming language can be equivalent; - 2+ years of professional experience: applied, research, or teaching experience is applicable; - Experience with numerical simulation methods; - Ability to design problems that mirror real physics research workflows; - Creative thinking in problem design across diverse physics areas; - Familiarity with physics modeling and approximation techniques; - Strong written English (C1+). How it works Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid Project time expectations For this project, tasks are estimated to require around 10–20 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. Compensation On this project, contributors can earn up to $76 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.

Are you an English language expert eager to shape the future of AI? Large‑scale language models are evolving from clever chatbots into powerful engines of linguistic discovery. With high‑quality training data, tomorrow’s AI can democratize world‑class education, keep pace with cutting‑edge research, and streamline communication for English speakers everywhere. That training data begins with you—we need your expertise to help power the next generation of AI. We’re looking for English language specialists who live and breathe the intricacies of English grammar, syntax, morphology, semantics, phonetics, and pragmatics. You’ll challenge advanced language models on topics like sentence structure, verb tense and aspect, word usage, idiomatic expressions, pronunciation, and cultural context—documenting every failure mode so we can harden model reasoning. On a typical day, you will converse with the model on language scenarios, verify factual accuracy and logical soundness, capture reproducible error traces, and suggest improvements to our prompt engineering and evaluation metrics. A degree in English language, linguistics, or a closely related field is ideal; peer‑reviewed publications, teaching experience, or hands-on linguistic analysis projects signal fit. Clear, metacognitive communication—“showing your work”—is essential. Ready to turn your English language expertise into the knowledge base for tomorrow’s AI? Apply today and start teaching the model that will teach the world. We offer a pay range of $6-to- $65 per hour, with the exact rate determined after evaluating your experience, expertise, and geographic location. Final offer amounts may vary from the pay range listed above. As a contractor you’ll supply a secure computer and high‑speed internet; company‑sponsored benefits such as health insurance and PTO do not apply. Employment type: Contract Workplace type: Remote Seniority level: Entry Level

ABOUT THIS PROFILE This role owns the full AI-powered go-to-market pipeline — from sourcing and qualifying leads through to outreach, pipeline management, and renewal risk detection. The engineer works upstream (data acquisition, ICP matching, database building) and downstream (outreach sequencing, CRM automation, churn prediction) to compound revenue growth and reduce manual overhead across Sales, Marketing, and Account Management. WHY THIS PROFILE MATTERS GTM is where revenue is made or lost. This role goes beyond just automating outreach — it starts with building the qualified lead database in-house, replacing external agencies with scalable AI-driven sourcing. Every week of delay is lost pipeline velocity. KEY RESPONSIBILITIES - Data Acquisition & Qualification Framework: Research and aggregate data sources across target markets, including licensing registries, government databases, and third-party data providers. Build a custom qualification framework to identify and verify target accounts. - ICP Definition & Lead Database Building: Define Ideal Customer Profiles based on industry, company size, region, and decision-maker roles. Build and maintain an enriched, verified database of qualified accounts and contacts, segmented by priority market. - Data Extraction & Enrichment: Extract data from official registries and public sources, enrich and segment accounts, research additional contacts on LinkedIn, and deliver verified databases aligned with the ICP. - AI SDR Outreach Sequencing: Design and deploy AI-driven outbound sequences that personalise messaging at scale, A/B test approaches, and auto-enrich prospect data from CRM and third-party sources. - Lead Scoring & Routing: Build and maintain models that score inbound and sourced leads against ICPs, integrating with CRM workflows to route high-intent leads in real time. - Churn & Renewal Risk Scoring: Develop predictive models that flag at-risk accounts 60–90 days before renewal, feeding proactive intervention workflows for Account Management. - Marketing Content Generation: Build automated pipelines for generating, reviewing, and publishing marketing content using LLM-based workflows. - Competitive Intelligence Monitoring: Set up automated systems that track competitor activity, pricing changes, and product launches. - Pipeline Hygiene & CRM Automation: Automate data entry, deal stage progression, activity logging, and reporting. Integrate qualified lead data directly into CRM systems and ongoing lead generation workflows. - Outbound Campaign Execution: Move beyond database building into executing AI-powered outbound marketing campaigns, including cold outreach, nurture sequences, and geographic expansion strategies. REQUIRED SKILLS - Python with LLM orchestration frameworks (LangChain, LlamaIndex, or equivalent) - CRM platforms (Salesforce, HubSpot) and their APIs, including data import/sync - Web scraping, data extraction, and enrichment tools (Apollo, Clay, Clearbit, LinkedIn Sales Navigator) - Workflow automation tools (n8n, Zapier, Make) and production integrations - Prompt engineering for reliable, production-grade LLM workflows - Database design for lead management and segmentation - GTM metrics (MQL/SQL conversion, pipeline velocity, churn rate, NRR) Nice to Have - Background in revenue operations, sales engineering, or growth engineering - Experience building custom data acquisition frameworks from public registries - ABM (Account-Based Marketing) strategies and tooling - Vector databases for semantic search and recommendation