Senior Software Engineer (Platform)

Toma•San Francisco, CA

About The Position

We're building the AI platform for underserved industries. LLM usage has seen a meteoric rise in the past year, but there is still a significant gap between agentic innovation and its use in the real world. This is especially true for underserved industries like automotive and healthcare, where outdated systems persist due to barriers to entry, legacy software, and high-stakes consequences of hallucinations and failure. Here at Toma (YC W24) , we are bridging this gap by providing a customer-centric platform to deploy and monitor AI agents, even for non-technical users. We recently raised a $17M Series A from a16z and are building the future of human-AI interactions, starting in the automotive industry. Our Team We’re assembling a team of Avengers: engineers, product managers, former founders, athletes, and leaders from Scale AI, Uber, Braze, Microsoft, Amazon, and more. We consider everyone regardless of their backgrounds or identities. Learn more about us here . About this Role We’re searching for a Senior Software Engineer to own and set technical direction for our core platform. You'll mentor engineers, collaborate closely with product and design, and help create fast, reliable, and magical user experiences. This is a deeply hands-on role—expect to write production code, review contributions, shape our architecture, and scale our platform as we grow.

Requirements

3+ years of experience in platform/infrastructure engineering
Strong background in system design, operating systems, and distributed systems
Deep expertise with AWS services (ECS/EKS, IAM, VPC, S3, RDS)
Experience with containerization and orchestration (Docker, Kubernetes)
Solid understanding of TypeScript

Nice To Haves

Track record of building and maintaining production ML/LLM infrastructure
Experience with observability tools (Prometheus, Grafana, ELK stack)

Responsibilities

Owning our entire infrastructure (monorepo with 10+ microservices on AWS and Porter )
Building and maintaining our ML/LLM model deployment pipeline and serving infrastructure
Owning our incident response process, including on-call rotations and alerting systems
Designing and implementing observability solutions across our stack
Partnering with other engineers to improve latency and performance in our realtime systems
Communicating with external vendors regarding cloud offerings and APIs
Upholding compliance endeavors (SOC 2 + GDPR + ISO 27001 in progress)
Managing cloud costs and optimizing resource utilization