• Build analytical and financial models that quantify the business impact of AI capabilities • Develop forecasts for AI adoption, usage, revenue influence • Construct business cases and decision-ready analysis to guide investment • Model and optimize cloud, GPU, and inference cost economics • Create executive- and Board-ready presentations that communicate strategic recommendations

Role Description Hence is hiring an AI Evaluation Lead to own how we measure the quality of AI-generated financial advice. Getting the advice right matters. A bad output here has real consequences for real people, and this role owns making sure we catch it. You will work with an AI-generated test case library and automated scoring infrastructure that is already in place. Your job is to: - Make sure we are measuring the right things. - Interpret what the results are telling us. - Determine what needs to change to keep the system performing well as it scales. This is not a monitoring and reporting role. It requires genuine judgment about AI system behavior, advice quality, and what the data is and is not capturing. You will report to our Head of Revenue & Compliance, work closely with the AI/ML team and founders, and partner with subject matter experts who provide domain judgment on complex or ambiguous cases. You need enough personal finance literacy to make first-pass quality assessments independently and know when to escalate. What You’ll Do (The Day-to-Day) - Define and validate the evaluation set: what cases we should be testing, whether coverage is sufficient across domains, and where the current framework has gaps. - Analyze scoring results to identify highest-frequency case types, patterns in what is performing well versus poorly, and anomalies that warrant closer review. - Assess whether current measures are detecting the right failure modes or whether new measures are needed. - Review flagged cases and make judgment calls on what the results mean and what should be done about them, drawing on both data and domain knowledge. - Own the criteria and calibration for when human review is triggered: defining what rises to that level, what does not, and ensuring the threshold stays well-calibrated as the platform scales. - Partner with subject matter experts on cases that require deeper domain judgment, and incorporate their input into evaluation design. - Ensure evaluation coverage keeps pace with new domain additions and model changes before they ship. - Translate findings into specific, actionable recommendations for the AI/ML team on what needs to change in the system. - Evolve the evaluation framework as the system grows, new domains are added, and user patterns shift. Qualifications - You have worked on AI or ML system quality in a context where outputs had real stakes. - You think analytically about what data is and is not telling you. - You are comfortable making judgment calls in ambiguous situations rather than waiting for the answer to be obvious. - You have enough AI/ML fluency to reason about why a system is producing what it is producing, not just whether the output looks right. - You bring enough personal finance literacy to read an advice response and have a genuine opinion about whether it is directionally sound. - You do not need formal credentials or deep expertise across every domain the system covers. - Fluency with how LLM-based systems behave in production, including output variance, failure modes, and the limits of automated scoring. - Ability to assess whether an eval framework is measuring the right things, not just whether it is running correctly. - Comfortable working with behavioral and interaction data to surface patterns and quality signals. - Familiarity with evaluation and observability tooling. Requirements - Model evaluation or QA on a consumer-facing AI product, particularly in a regulated or high-stakes context. - Model risk or validation with LLM or generative AI exposure. - Data science or analytics with ownership of production AI system quality. - Operations quality control built around AI- or ML-generated outputs. - Financial services or fintech product roles where you developed both analytical depth and personal finance domain familiarity. This is probably not the right role for you if: - Your background is primarily in building models rather than evaluating what they produce. - Personal finance is entirely unfamiliar territory. - You are looking for a well-defined role with stable processes. - You default to manual review rather than thinking systematically about what should be automated and what requires human judgment. How we work We are a fully remote, distributed team. Periodic in-person get-togethers will be integral to our operating cadence. We’re adults who prioritize outcomes and output over set schedules. We value clear writing, high ownership, fast iteration, direct communication, and thoughtful async collaboration. As an early team member, you should expect broad ownership, frequent context shifts, and a high degree of autonomy. You will help shape not just the product, but also the technical standards and operating cadence of the company. Compensation Salary: 120-140k, plus early-stage option equity. Final compensation will depend on level, experience, location, and scope of responsibility. This role is open to candidates based in the United States. Equal Opportunity & Accommodations Hence is proud to be an equal opportunity employer. We do not discriminate in hiring or any employment decision based on race, color, religion, national origin, age, sex (including pregnancy, childbirth, or related medical conditions), marital status, ancestry, physical or mental disability, genetic information, veteran status, gender identity or expression, sexual orientation, or other applicable legally protected characteristic. Hence is also committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If you need assistance or an accommodation due to a disability, please let your recruiter know.

Role Description Senior AI/ML Architects at Mayo Clinic serve at the leading edge of data, systems, and computer sciences, embodying the convergence of technological expertise and visionary innovation. In our dynamic and collaborative environment, Senior AI/ML Architects are pivotal in transforming healthcare, advancing patient care through cutting-edge AI solutions. By joining our team, you will navigate the complexities of AI technologies, multimodal AI architectures, data architecture, and system integration, crafting scalable, impactful AI models and infrastructures that drive forward medical breakthroughs and improve patient outcomes. This includes guiding architectural decisions related to foundation models, vision-language systems, representation learning, model interpretability, and the integration of diverse healthcare data modalities. As part of our dedicated team, you will work alongside world-class professionals, leveraging state-of-the-art resources to solve real-world challenges. Our commitment to excellence and innovation provides an unparalleled platform for professional growth and the opportunity to make significant contributions to the field of healthcare. If you are passionate about harnessing the power of AI to make a difference, we invite you to explore our AI/ML Architect job family, which includes AI/ML Architect, Senior AI/ML Architect, and Principal AI/ML Architect positions. Each role offers a unique opportunity to influence the future of healthcare technology and improve lives on a global scale. - Providing strategic direction and technical leadership for complex AI Engineering initiatives. - Designing and implementing enterprise-wide AI models and solutions that address complex healthcare challenges, improving patient care and outcomes. This includes evaluating and guiding the use of foundation models, multimodal learning approaches, vision encoders, and AI architectures that leverage clinical text, medical imaging, genomic, and other healthcare data sources. - Collaborating with cross-functional teams, including data scientists, engineers, and healthcare professionals, to seamlessly integrate AI technologies into our systems. - Evaluating and recommending new technologies to enable continuous innovation across Mayo Clinic, including advances in multimodal AI, vision-language models, foundation models, representation learning, model interpretability, and emerging AI architectures relevant to healthcare. - Contributing to the development of scalable and efficient data architectures, ensuring the integrity and accessibility of healthcare data. - Participating in the entire lifecycle of AI solution development, from concept to deployment, including problem identification, system architecture design, implementation, and evaluation. Providing architectural guidance on model selection, multimodal fusion strategies, evaluation methodologies, explainability, scalability, and deployment considerations. - Leading the collaboration with cross-functional teams, including clinicians, translation engineers, software engineers, and product managers, to gather requirements, assess feasibility, define the scope, requirements, and deliverables of AI projects. - Providing consultative services to departments and divisions, offering insights and strategies to address complex business problems. - Evaluating and guiding architectural decisions for advanced AI systems, including multimodal foundation models, vision-language architectures, representation learning approaches, embedding strategies, and early-, mid-, and late-fusion methodologies for heterogeneous healthcare data. - Providing mentorship, guidance, and technical leadership to junior architects and engineers within the AI enablement team. May have supervisory responsibilities. Benefits - Medical: Multiple plan options. - Dental: Delta Dental or reimbursement account for flexible coverage. - Vision: Affordable plan with national network. - Pre-Tax Savings: HSA and FSAs for eligible expenses. - Retirement: Competitive retirement package to secure your future.

• Design and ship AI agents that replace high-volume manual marketing workflows • Rebuild marketing operations infrastructure with predictive lead scoring, lifecycle management, and agent-forward workflows that increase business efficiency • Wire together the marketing stack — Salesforce, HubSpot, Qualified, Clay, ZoomInfo, and our data warehouse • Build personalization and testing tools that improve campaign performance at scale • Own the systems you ship: monitor them, build fallback logic, instrument for observability, iterate on results • Work directly with marketing leadership, RevOps, and sales to identify the highest-leverage problems and translate them into working software