Engineer Manager, Platform

Knowtex is building the future of voice AI operating systems for clinicians, transforming how healthcare documentation happens at the point of care. We are experiencing rapid growth across both commercial health systems and federal healthcare, with our ambient documentation platform scaling to thousands of clinicians across hundreds of specialties.

We are at an inflection point where advances in speech, language models, and clinical AI can fundamentally change how clinicians interact with technology, giving them more time to focus on what matters most: their patients.

We are hiring an Engineering Manager, Platform to lead the team responsible for the backend systems, infrastructure, and shared technical foundations that power Knowtex.

This is a highly hands-on engineering leadership role. We are looking for someone who has successfully managed engineers before but still wants to design systems, write production code, review architecture, debug difficult problems, and directly contribute to critical projects.

The Platform team owns core backend services, cloud infrastructure, reliability, scalability, data systems, and shared engineering capabilities used across Knowtex's products. The team also currently owns several applied ML engineering initiatives and works closely with our growing ML organization.

You will report directly to the Head of Engineering and own both the technical direction and day-to-day execution of the Platform team.

This is not a pure people-management role. You should be comfortable moving between managing engineers, setting technical direction, and personally jumping into the highest-priority engineering problems.

• Lead, manage, and develop a team of platform and backend engineers

• Own execution and delivery across Platform projects

• Set clear priorities, break down ambiguous projects, and ensure projects move from design through production

• Run effective technical planning, design reviews, and engineering reviews

• Coach senior and junior engineers and help raise the technical bar across the team

• Partner closely with the Head of Engineering on platform strategy, architecture, hiring, and organizational priorities

• Identify execution risks early and take ownership of resolving them

• Design and build production backend and platform systems alongside the team

• Take direct ownership of technically difficult or high-priority projects when needed

• Review architecture, code, infrastructure changes, and technical designs

• Debug complex production and distributed-system issues

• Make pragmatic architectural decisions that balance speed, reliability, scalability, and maintainability

• Establish patterns and technical standards that other engineers can build on

• Own the reliability, scalability, and performance of Knowtex's backend platform

• Design systems capable of supporting rapidly growing clinical workloads

• Improve observability, monitoring, alerting, and incident response

• Own cloud infrastructure and help improve security, cost efficiency, and operational resilience

• Build shared services and infrastructure that allow product engineers to move faster

• Drive improvements to developer experience, deployment workflows, testing, and engineering productivity

• Identify and eliminate technical bottlenecks before they become scaling problems

• Partner closely with ML engineers and researchers to productionize new AI capabilities

• Build infrastructure for model inference, evaluation, experimentation, and data processing

• Support applied ML initiatives that currently live within the Platform organization

• Help establish scalable technical foundations for Knowtex's growing ML organization

• Design systems that balance AI quality with latency, reliability, scalability, and cost

• 7+ years of professional software engineering experience

• Previous experience directly managing a software engineering team

• Strong hands-on backend engineering experience and willingness to continue writing production code

• Deep experience designing and operating distributed backend systems

• Strong system design and software architecture skills

• Experience building production systems on AWS or another major cloud platform

• Experience owning production systems with meaningful scale, reliability, and availability requirements

• Strong understanding of databases, APIs, asynchronous systems, queues, caching, and distributed architectures

• Experience with infrastructure, observability, deployment systems, and production operations

• Demonstrated ability to lead complex technical projects from ambiguity through production

• Ability to effectively manage both senior and junior engineers

• Strong communication skills and ability to work across engineering, product, ML, and business teams

• Experience managing a Platform, Infrastructure, Backend, or Developer Infrastructure team

• Staff-level or equivalent technical experience prior to or alongside engineering management

• Experience with Python and modern backend frameworks

• Experience with AWS services such as Lambda, ECS/EKS, SQS, RDS, DynamoDB, S3, CloudWatch, or related technologies

• Experience operating event-driven or asynchronous distributed systems

• Experience building systems with strict latency and reliability requirements

• Experience with containers, infrastructure-as-code, and modern CI/CD systems

• Experience building data infrastructure or large-scale processing pipelines

• Experience supporting ML systems, model inference, LLM applications, or AI infrastructure

• Experience in healthcare technology or other regulated environments

• Experience with HIPAA-compliant systems and healthcare data

• Experience working in fast-moving startup environments where engineering leaders remain deeply involved in execution

• The Platform team has clear ownership, priorities, and technical direction

• Engineers can execute independently without requiring constant escalation to senior leadership

• Critical backend and infrastructure projects are delivered predictably

• Platform reliability and scalability improve as Knowtex grows

• Production issues are detected and resolved quickly, with recurring problems systematically eliminated

• Product and ML teams can move faster because the underlying platform provides strong abstractions and infrastructure

• You remain technically close enough to the systems to make high-quality engineering decisions while building a team that can operate effectively without depending on you for every decision

• AWS

• Python

• Distributed backend systems

• Event-driven and asynchronous architectures

• Relational and NoSQL databases

• Containers and cloud infrastructure

• Infrastructure as code

• CI/CD and automated deployment

• Observability, monitoring, and incident response

• LLM and ML inference pipelines

• Large-scale clinical data processing

• HIPAA-compliant production environments

• Competitive salary

• Meaningful equity compensation

• Unlimited PTO

• Premium health, dental, and vision coverage

• 401(k) plan

• Work model: Hybrid In-person (Monday, Tuesday, Wednesday in office in SF)