• Performing diverse data-related tasks, including: • Data collection, evaluation, and annotation. • Pairwise comparisons. • Counting tasks. • Object tagging and labeling across different content types (audio, video, images, or collected data)

Role Description This opportunity involves designing computational physics problems to challenge a frontier AI model. The problem must have an answer verifiable by code and require a specialized tool like FEniCS, OpenFOAM, Meep, REBOUND, CAMB, or others. Each problem runs inside a sealed Linux container with the tool pre-installed and a programmatic judge that grades the model's answer. - Pick an anchor tool and design a problem that hinges on its physics models, integrators, Monte Carlo kernels, or PDE discretisations. - Write a Python reference solution, supply input files and domain or initial condition definitions where needed. - Decide the numerical answer and how close the model needs to get — with a domain-appropriate tolerance — to count as right. - Test the problem against the model in batches of parallel attempts, tuning the problem difficulty until the agent only succeeds in a small number of attempts. - Once you're happy with the task, and it scores within range, the task goes to a senior reviewer in your subfield for feedback to ensure task quality is high. Calibration requires patience. You're tuning the problem against batches of parallel runs of the agent, aiming for a pass rate in the 10–30% band. Reaching that means rewriting field configurations, tightening initial conditions and solver parameters, and watching how the agents act. You'll learn how these agents cut corners, where a simulation stalls, and where an integrator converges. This time compounds in two directions, giving you deeper command of the anchor tool and a hands-on working intuition for how a frontier model navigates complex electromagnetic, fluid, gravitational, and cosmological problems. Qualifications - Degree in Physics (Theoretical, Experimental, or Computational) or related field. - 2+ years of research, applied, or teaching experience. - Python proficiency for writing reference solutions. - Fluency with — or strong willingness to independently learn — at least one scriptable physics package: FEniCS / DOLFINx, OpenFOAM, Meep, MPB, openEMS, Geant4, PYTHIA8, ROOT / PyROOT, WarpX, REBOUND, MESA, CAMB, CLASS, or bilby. - Ability to design problems that genuinely require a specialized simulation tool. - Strong written English (C1+). Requirements - No prior experience with the listed tools? You're still welcome to apply — as long as you're ready to get up to speed on your own and hit the ground running. Benefits - Contributors can earn up to $35 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. Company Description 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.

• Researching and evaluating AI tools applicable to legal practice operations (e.g. document automation, AI agents) • Building and maintaining workflow automations using tools such as Make.com, Zapier or similar platforms • Supporting integration and optimisation of ActionStep and NetDocuments • Identifying inefficiencies in existing processes and proposing practical solutions • Producing internal reports and dashboards to support practice management decisions • Providing general technology support and troubleshooting across the team's software stack • Managing ad hoc research and project coordination tasks assigned by the principal • Supporting legal assistant functions during quiet periods (document preparation, file management, data entry)

• You will own, build, and run your startup in fields such as AI • You will embark on an extensive personal development journey crafted by unicorn founders • You will receive support in hiring through our network of over 50,000 professionals • You will receive intensive coaching to make your startup ready to raise millions in funding • You will iterate your product with us until having reached product-market-fit