Jobs at Hippocratic AI

Own end-to-end AI data labeling and evaluation programs supporting frontier healthcare models. Responsibilities include designing annotation tasks and rubrics, managing clinician and expert rater workforces, overseeing vendors, building quality-control systems, tracking throughput and costs, and coordinating ML, engineering, clinical, and infrastructure teams. The role manages supervised, audio, conversational, preference, RLHF, and evaluation data pipelines while improving quality and delivery speed.

Owns the UX and interface design of an internal labeling and annotation tool. Responsibilities include redesigning workflows to improve efficiency and accuracy, prioritizing interface changes, defining performance metrics, applying gamification and behavioral psychology, managing reward and feedback mechanics, and partnering with technical product managers and engineering to deliver improvements.

Own the end-to-end reinforcement learning and on-policy distillation post-training pipeline for healthcare-focused large language models. Design RL methods, build reward models and verifiers, develop conversational healthcare simulations, automate post-training with agents, and conduct rigorous experiments. Collaborate with research, engineering, and clinical teams to improve model reasoning, safety, and alignment.

Own the company’s three-statement financial model, P&L, balance sheet, cash flow, gross margin, COGS, GPU/cloud, headcount, contractor, software, and G&A forecasts. Lead variance analysis, KPI reporting, cost optimization, unit economics, board and investor materials, and financial model reconciliation with accounting, HR, engineering, recruiting, and business teams.

Own the company’s IT and workplace functions, including identity, endpoints, SaaS, networking, security controls, compliance, budgets, facilities, physical security, AV, vendors, and business continuity. Lead and develop the team, establish service levels, automate provisioning and access governance, manage audits, support remote staff, and oversee technology and workplace integration during acquisitions.

Build tailored demos and prototypes for an agentic AI platform, write code and integrations, configure agent workflows, and lead technical narratives during enterprise sales cycles. Conduct discovery, design solutions, present to clinical and business stakeholders, support pilots, and communicate field insights to Product and ML teams. The role requires strong coding, consulting, presentation, storytelling, and client-facing skills in a full-time Menlo Park office environment.

Leads enterprise growth marketing, ABM, digital acquisition, marketing operations, MarTech, revenue attribution, analytics, and budget strategy. Owns demand generation, marketing data architecture, lead routing, CRM integration, conversion optimization, executive reporting, and AI-enabled targeting and automation. Partners with Sales, RevOps, Legal, Security, Compliance, and Customer Success to generate qualified healthcare enterprise pipeline and improve revenue impact.

The LLM Inference Engineer will optimize large language model infrastructure and implement serving architectures, quantization techniques, and latency optimizations. Responsibilities include designing disaggregated serving solutions, benchmarking system performance, and guaranteeing efficient, scalable LLM systems in production.

Build and operate distributed LLM inference systems, including disaggregated prefill/decode serving, KV-cache transfer, multi-node GPU execution, networking, benchmarking, observability, and Kubernetes production deployment. Optimize latency, throughput, reliability, and cost across compute, memory, network, and storage layers. Diagnose complex distributed failures and collaborate with model, infrastructure, and application engineering teams to launch models and hardware safely.

Build and scale AI-powered healthcare voice-agent SaaS products across front-end and back-end systems. Develop APIs, dashboards, mobile applications, reusable frameworks, and secure user experiences using TypeScript, React, Python, and cloud technologies. Collaborate with AI researchers, product managers, designers, and healthcare professionals; conduct code reviews, mentor engineers, resolve technical bottlenecks, and support agile continuous delivery.

Lead Hippocratic AI's social media presence across LinkedIn, TikTok, and X: build TikTok from scratch, create executive and brand content, produce short-form video and graphics, manage content calendar and influencers, source stories from cross-functional teams, use AI agents and analytics to optimize performance, and support broader comms and marketing deliverables.

Own the payor and provider vertical product strategy, identifying high-value healthcare workflows and translating customer, regulatory, clinical, and operational needs into roadmap priorities. Partner with customers, Engineering, clinical safety, Sales, and Marketing to define ROI, ship safe AI voice solutions, and transition use cases from pilot to production. Build value propositions, ROI models, and case studies while driving adoption and expansion.

Lead company-wide product analytics and AI evaluation strategy, defining success metrics, quality baselines, and analytical frameworks. Build ROI and business models connecting healthcare AI adoption to clinical outcomes, cost reduction, and value-based care. Analyze product and program impact, improve data instrumentation, and translate complex findings into executive recommendations. Partner with Product, Engineering, Customer Success, clinical leaders, and healthcare customers while influencing roadmaps, investment decisions, and analytics standards.

Lead Hippocratic AI’s integration engineering function, overseeing architecture, delivery, reliability, and production support for Epic, Cerner, athenahealth, HL7, FHIR, and related interfaces. Manage distributed US and India teams, establish engineering and incident-response practices, guide partner integration launches, and ensure monitoring, documentation, and stable production operations. Collaborate with product, partnerships, health system IT, and clinical informatics teams while maintaining the organization’s interoperability roadmap.

Own product positioning, messaging, and go-to-market strategy for enterprise agentic AI products in healthcare. Lead launches, develop sales enablement and customer-facing content, build buyer personas, monitor market intelligence, and translate complex AI capabilities into credible business value. Partner cross-functionally with Product, Sales, Customer Success, Legal, and Compliance while ensuring messaging meets clinical, regulatory, and brand standards.

Develop and deploy autonomous clinical conversation workflows, including prompt engineering, conversation logic, safety guardrails, and integrations with EHRs, scheduling systems, and telephony platforms. Analyze model outputs and user outcomes, collaborate with clinicians and engineering teams, maintain production code, and support customer go-lives. The role begins as a residency program and may progress into Forward Deployed Engineering, Engineering, or Product.

Design and operate a GPU management and scheduling platform for approximately 30 AI models across heterogeneous hardware. Build metrics pipelines, admission control, autoscaling, cloud orchestration, infrastructure automation, deployment pipelines, and monitoring systems. Develop production software in Python and Go, operate secure fault-tolerant cloud infrastructure, enforce healthcare security and compliance policies, troubleshoot complex issues, and mentor engineers.

Lead end-to-end post-training for LLMs using reinforcement learning and on-policy distillation to improve clinical reasoning, safety, and alignment. Design RL/OPD methods, build reward models and verifiers, create healthcare conversational environments with synthetic data, automate post-training loops, run experiments, and collaborate with research, engineering, and clinical teams to deploy models at scale.

As a Senior Staff AI Engineer, define multi-year technical strategy, architect platforms for voice-based AI in healthcare, mentor engineers, and represent the company in external discussions.

Embed with healthcare customers to scope, build, and deploy production-grade conversational AI (RAG, agents, real-time voice) integrated with EHRs and cloud infrastructure, own monitoring and iterative improvements, and feed field insights back into product and research.