Jobs at AKASA

Leads customer implementation pods from contract signature through deployment and post-implementation support. Manages 6–8 concurrent software projects, coordinating cross-functional teams, timelines, risks, dependencies, communications, and status reporting. Uses internal dashboards to demonstrate customer ROI and monitor product health. The role requires enterprise client-facing experience, technical implementation expertise, creative problem-solving, and strong organizational and communication skills. It is based in the San Francisco Bay Area or New York City with 2–3 in-office days weekly and minimal occasional travel.

Translates complex clinical scenarios into product and engineering requirements for healthcare AI solutions. Analyzes clinical cases and databases, designs digital experiments, develops revenue cycle training specifications, supports customer proof-of-concept work, validates product specifications, and coordinates project resources and deliverables. Requires substantial clinical documentation improvement experience across inpatient and outpatient settings, including concurrent and retrospective reviews, coding, quality assurance, documentation, and training.

Lead technical program management for generative AI healthcare products: plan initiatives, translate product requirements into technical specs, manage SDLC and scrum, coordinate UAT and deployments, track schedules and risks, and drive cross-functional execution and stakeholder communication.

The Chief of Staff will support the COO and CEO, drive strategic initiatives, ensure operational alignment, and facilitate decision-making in the fast-paced AI healthcare space.

As a Client Solutions Software Engineer, you will implement, support and optimize ML model deployments for healthcare revenue cycles while collaborating with various teams on technical integrations and product improvements.

The Sr. Machine Learning Engineer will develop and deploy ML solutions for healthcare, manage data pipelines, and work with large datasets to enhance healthcare delivery.

Sell AKASA's AI-native revenue cycle management solutions to health systems. Build C-level relationships, drive business development, lead full sales cycle from prospecting to close, deliver consultative presentations, work with BDRs, attend industry events, and collaborate cross-functionally. Travel domestically up to 60%.

Own and lead a flagship clinical documentation and medical coding product within an AI-native suite. Define strategy, evaluate opportunities, create roadmaps and PRDs, and drive execution with engineering and ML teams. Partner with customers, design partners, and cross-functional teams to launch, measure, and iterate on products that improve documentation quality and revenue cycle outcomes.

Generate and qualify top-of-funnel opportunities through outbound prospecting and inbound lead follow-up. Schedule initial commercial meetings, maintain Salesforce records, coordinate with marketing and sales, attend select industry events, and surface operational issues to help advance pipeline and support revenue growth.

Own end-to-end QA and product operations for one or more product areas: design and maintain regression suites, triage support issues, define go/no-go release criteria, monitor support metrics, and route high-impact incidents to PMs and engineers.

Lead and scale AKASA's compliance and AI governance: own HITRUST, SOC 2, and HIPAA programs; operationalize NIST AI RMF; drive automation-first GRC and continuous control monitoring; manage policy lifecycle, vendor risk, customer audits, and sales security support; partner with Engineering, Legal, and Security.

As a Sr. Software Engineer, Backend, you will develop AI-powered solutions for clinical documentation and medical coding, collaborating with cross-functional teams to enhance SaaS products and deliver actionable insights for healthcare organizations.

The role involves managing and optimizing infrastructure using Terraform and Kubernetes, ensuring system reliability and observability, and collaborating with software engineers to enhance operational excellence.

Deploy and maintain ML/LLM models for healthcare revenue cycle clients: handle implementation from requirements through rollout and production support, run LLM evaluation frameworks, integrate data pipelines and databases, and collaborate with client engagement, product, and R&D teams to improve platform performance.