• Design, build, and improve ML systems for recommendations, ranking, personalisation, retrieval, and GenAI workflows; • Turn product goals into concrete ML problems, evaluation plans, experiments, and shipped features; • Work with behavioural, transactional, contextual, and unstructured data to identify signals and improve model quality; • Build offline evaluation frameworks and online experiments to measure relevance, quality, latency, cost, and business impact; • Improve GenAI agent behaviour through better retrieval, context management, prompting, tool use, orchestration, and evaluation; • Investigate failure modes, run error analysis, and make practical tradeoffs across quality, reliability, speed, and complexity; • Partner closely with platform and backend engineers to deploy, monitor, and iterate on models in production; • Help define how the company does ML: metrics, experimentation discipline, technical standards, and long-term direction; • Work with real-time behavioural and transactional signals to improve recommendations, personalisation, and intelligent product behaviour; • Contribute to predictive and insight-driven ML use cases such as segmentation, churn prediction, recommendation measurement, and opportunity ranking; • Write clean, testable Python and contribute reusable ML components and shared libraries used across the platform.

• Spearhead LLM agents, realtime anomaly detectors, and other breakthrough solutions that solve real-world problems and create product magic. • Collaborate with product, engineering, ops, and data science to spot high leverage opportunities, refine AI/ML requirements, make principled architecture choices, and measure business value with clear, data-driven metrics. • Design, train, deploy, and operate large-scale AI applications for both batch and streaming workloads, ensuring low latency, high reliability, and continuous improvement via automated monitoring and retraining loops. • Mentor and inspire teammates, fostering a collaborative, experimentation-driven environment where cutting edge research meets production excellence and every engineer is empowered to push AI boundaries at Airbnb.

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 chemistry problems that simulate real chemistry research workflows; - Create problems requiring Python programming to solve (using numpy, scipy, chemical libraries); - Ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks); - Develop problems requiring non-trivial reasoning chains in physical chemistry, quantum chemistry, and molecular modeling; - Base problems on real research challenges or practical applications from chemistry practice; - Verify solutions using Python with standard computational chemistry approaches; - Document problem statements clearly and provide verified correct answers. What we look for This opportunity is a good fit for chemistry specialists with an experience in python open to part-time, non-permanent projects. Ideally, contributors will have:   - Degree in Chemistry 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 methods in chemistry; - Familiarity with computational chemistry concepts and methods; - 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.

• Develop AI‑powered applications using state‑of‑the‑art LLMs • Build and deploy applications for internal customers, selecting the right models (e.g., GPT‑4o, Claude Sonnet 4, Gemini 2.5 or open‑source alternatives) • Implement retrieval‑augmented generation (RAG) pipelines and agents using LangChain, LangGraph, LlamaIndex, and other emerging frameworks • Integrate ML models with front-end apps, REST APIs, and backend systems • Design and build end-to-end AI systems—ranging from chatbots and recommendation engines to vision-based pipelines • Build robust logic around ML models to address edge cases, failovers, and complex business rules • Leverage prebuilt models or services (e.g., OpenAI API, Azure Cognitive Services, HuggingFace) to accelerate development cycles • Fine‑tune and customize models using LoRA, prompt tuning, or RLHF techniques • Occasionally perform custom model training—but more often fine-tune or adapt existing models to meet business needs • Work with vector databases (e.g., Pinecone, Weaviate) and explore graph-based approaches (e.g., GraphRAG, Knowledge Graphs) • Ensure privacy and compliance with HIPAA, including encryption, PHI protection, and vendor due diligence • Prototype multi‑agent systems using frameworks like CrewAI or Stands SDK and evaluate emerging agent orchestration solutions • Collaborate cross-functionally with product managers, data scientists, and compliance teams • Mentor junior engineers, lead innovation discussions, and continuously evaluate emerging LLM architectures