Jobs at Translucent

Design and refine complex healthcare finance workflows for an AI platform, partnering closely with product engineers and the founding designer from concept through production. Responsibilities include UX and interaction design, visual polish, reusable components, design-system improvements, production-ready prototyping, design QA, and iteration based on customer and usage feedback.

Develop clinical operations expertise for AI products focused on patient flow, throughput, capacity, and utilization. Define workflows, benchmarks, datasets, rules, and evaluation methods for AI agents analyzing length of stay, operating room utilization, emergency department flow, imaging and lab capacity, and discharge barriers. Collaborate with engineering, product, design, and customer delivery teams to translate clinical and operational challenges into technical solutions and validate model outputs against real-world provider operations.

Owns security and compliance across IT, software development, GCP infrastructure, and subprocessors. Responsibilities include managing SecOps tools, improving software security posture, conducting security and AI audits, maintaining compliance artifacts, developing data and AI governance, vetting technologies, and managing contractual security obligations. The role partners with engineering teams and requires experience with Vanta, cloud DevOps, infrastructure as code, identity platforms, security reviews, and regulated industries.

Lead product design for an AI financial platform serving healthcare organizations. Own UX architecture, interaction design, prototyping, visual refinement, and design systems. Translate complex financial and operational workflows into intuitive experiences, collaborate with product and engineering, validate designs through user research and usage signals, and mentor designers. This founding role requires hands-on execution, strategic ownership, and rapid prototyping in an ambiguous startup environment.

Build responsive, accessible frontend and full-stack features for an AI-powered healthcare finance platform. Partner with Product, Design, and AI Engineering to create data-rich interfaces, contribute to the design system, integrate APIs and LLM workflows, and improve frontend reliability and performance. The role requires strong product judgment, end-user empathy, accessibility expertise, and ownership of features from conception through customer impact.

Build and validate SQL transformations mapping healthcare financial and clinical data into a unified ontology and semantic layer. Reverse-engineer customer business rules, own testing and data correctness, manage historical backfills and incremental loads, and collaborate with customers and platform, insights, and product engineering teams. The role requires strong healthcare revenue-cycle data expertise, production Python, modern data-platform experience, transformation-as-code workflows, data modeling, and customer-facing requirements discovery.

Own healthcare finance products from discovery through launch, onboarding, adoption, and measurable financial impact. Partner with healthcare customers, engineering, design, deployment, and delivery teams to define problems, write specifications, prototype solutions, manage AI agent behavior and evaluation criteria, triage issues in Linear, and oversee QA. Drive activation, usage, customer training, and dollars recovered or protected while rapidly iterating in an ambiguous startup environment.

Serve as the healthcare revenue cycle subject matter expert for AI product development. Define workflows, rules, benchmarks, datasets, and quality standards for denial classification, underpayment detection, reconciliation, charge capture, and prior-authorization triage. Partner with engineering, product, design, and customer delivery teams to translate operational requirements into technical solutions, validate model outputs, and establish ground truth through detailed account review and adjudication.

Build and configure AI agents and ML models for healthcare finance customers using existing platform infrastructure. Partner with customers and product teams to scope solutions, create visualizations, present AI capabilities, validate model reliability, and establish testing rigor. Generalize customer-specific solutions into reusable platform features while shipping changes through code review and automated CI/CD.

Build and maintain the foundational GCP and Kubernetes platform supporting data and agentic AI systems. Develop well-tested applications in Python, TypeScript, and Golang; manage GKE deployments, GitOps workflows, infrastructure as code, databases, migrations, and secure application delivery. Partner with product engineers, optimize scaling and LLM-assisted coding workflows, and validate system correctness and security. The role is based in New York City and requires in-office work four days per week.

Own technical implementation of the Physician Enterprise product across enterprise healthcare accounts. Lead requirements gathering, scope, delivery plans, quality decisions, pod prioritization, risks, milestones, and post-launch transition. Partner with Product and Engineering on roadmap development and validate new capabilities. Translate physician productivity, labor costs, service-line performance, and compensation needs into technical solutions while building scalable implementation playbooks.

Own a healthcare finance product vertical end to end, including its strategy, roadmap, AI agents, customer adoption, quality, and realized financial value. Partner with healthcare executives, engineering, sales, and marketing to identify high-value problems, build prototypes, ship products, and validate outcomes. The role requires deep provider finance or operations expertise, production AI experience, strong data fluency, executive presence, and the ability to operate hands-on in an early-stage startup.

Build and scale Translucent’s production agentic AI platform for healthcare finance. Responsibilities include standardizing tool and connector interfaces, integrating agents across products, developing evaluation harnesses and benchmarks, engineering context and control loops, fine-tuning and evaluating models, and delivering reusable AI capabilities. The role requires production Python engineering, LLM or agent experience, retrieval and data-layer expertise, and a strong bias toward shipping reliable systems.

Develop physician enterprise subject-matter expertise for AI product development and customer margin improvement. Build benchmarks and datasets for productivity, compensation, billing, access, and workflow analysis; evaluate AI agents against real-world medical group tasks; and translate physician operations, financial, and clinical requirements into product and engineering solutions. Collaborate with customers, physicians, product, design, engineering, and delivery teams while auditing model outputs and provider-level workflows.

Owns end-to-end AI platform deployments for enterprise health systems, including scoping, data integration, configuration, validation, QA, launch, and adoption. Coordinates CFOs, data teams, IT, clinical operations, engineers, product, and customers. Builds trust in AI outputs, manages project plans and accountability, resolves integration issues, and converts deployment learnings into reusable playbooks, templates, and product feedback.

Own healthcare finance products end to end, from customer discovery and problem definition through shipping, onboarding, adoption, and measurable financial impact. Partner with customers, engineering, AI, design, and delivery teams; write product specifications, prototype, define agent autonomy and evaluation criteria, manage issue triage, oversee QA, and conduct training. The role requires strong quantitative product ownership, technical fluency, rapid execution, and comfort operating in an ambiguous startup environment.

Own a healthcare finance product vertical from thesis and roadmap through AI agent development, customer adoption, realized financial value, and team growth. Partner with healthcare executives, AI Engineering, platform teams, and GTM to deliver data-heavy products, trustworthy automation, pricing, and positioning. The role requires hands-on prototyping, specifications, QA, onboarding participation, and accountability for measurable customer outcomes.