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San Francisco, California, United States
Hybrid
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)
