• Design, build, and continuously improve a curriculum of AI training programs spanning foundational literacy, role-specific fluency, and advanced practitioner development • Partner with functional leads across the business to understand skill gaps and tailor learning pathways to specific roles (advisory, delivery, operations, etc.) • Develop and manage self-paced resources, workshops, certifications, and onboarding materials that scale across a distributed workforce • Track learning outcomes and adoption metrics, using data to continuously sharpen what's working • Build and run Coalfire's internal AI community — creating spaces (channels, forums, events, office hours) where employees can share what they're building, ask questions, and learn from each other • Identify and cultivate internal AI champions and power users across the organization • Facilitate knowledge-sharing and peer learning so best practices spread organically, not just top-down • Be the voice that keeps AI top-of-mind internally — translating complex or abstract AI developments into relatable, relevant narratives for a general employee audience • Develop and distribute internal communications (newsletters, announcements, spotlights) that highlight real AI wins, use cases, and learnings from across the company • Represent the AI & Data team at all-hands meetings, internal events, and cross-functional forums • Serve as a thought partner to the Chief AI & Data Officer on the human dimensions of our AI strategy — readiness, culture, change management • Collaborate with HR and People teams on integrating AI competencies into talent development frameworks, performance expectations, and hiring • Stay current on enterprise AI trends, tools, and adult learning best practices, bringing relevant insights back to the team

• Review AI-extracted data from insurance submissions (SOVs, loss runs, supporting documents) for accuracy, completeness, and consistency. • Compare extracted fields against source documents, identify discrepancies, and correct data directly in the appropriate systems or templates. • Act as a quality gate for the AI pipeline, ensuring output meets agreed business and underwriting expectations before it moves downstream. • Log issues, defects, and edge cases with clear reproduction steps, examples, and impact, using tools like Jira or similar. • Identify patterns and root causes behind extraction errors (e.g., recurring issues with specific formats, document types, or fields). • Translate observed patterns into well-structured requirements, user stories, and bug reports that engineering and data teams can act on. • Collaborate closely with architects, data engineers, and other analysts to refine extraction rules, templates, and workflows. • Use LLMs and AI assistants as tools (e.g., for summarization, cross-checking, hypothesis generation), while exercising sound judgment about what to trust and what to verify. • Help continuously improve documentation, checklists, and guidelines for reviewing submissions and extractions. • Over time, contribute to defining metrics and dashboards for data quality and model performance (e.g., accuracy by field, error rates by document type).

Role 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. What this opportunity involves: - You design 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. As an expert author, you: - 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. - Watching how the agents act. This time compounds in two directions. You come out of each task with deeper command of the anchor tool itself, and also get 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+). 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. Requirements - This opportunity is a good fit for physicians with experience in Python open to part-time, non-permanent projects. Benefits - For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements. - 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.

• Lead end-to-end delivery of Data & AI programs across planning, execution, and governance • Build relationships with executives and key stakeholders to align on outcomes • Translate technical solutions into business value and strategy • Drive program governance including scope, risks, and dependencies • Oversee Microsoft Fabric implementations and data platform modernization • Coordinate cross-functional teams (engineering, analytics, AI) • Facilitate executive steering committees and decision forums • Develop adoption and change management strategies • Monitor delivery performance using KPIs, dashboards, and reporting • Support business development and client engagement activities