Emergent Labs
Software Engineer - Infrastructure
San Francisco
Sponsorship not specifiedDetected 23 days ago
PythonGoDistributed SystemsGitSQLNoSQLPostgreSQLMySQLMongoDBRedisDynamoDBAWSGCPKubernetesTerraformHelmCI/CDGitHub ActionsLinuxPrometheusGrafanaDatadogPlatform EngineeringKafka
About the role
- Since our public launch, we've crossed $100M in ARR and grown to over 10M users across 190+ countries.
- We're backed by Khosla Ventures, SoftBank, Google, Lightspeed, Prosus, Together, and Y Combinator.
- The team is built by repeat founders, Olympiad medalists, IIT & IIM alumni, and leaders from Google, Amazon, and Dropbox.
Responsibilities
- Maintain stability of our platform consisting of distributed microservices closely interacting with Kubernetes and cloud providers (GCP, AWS)
- Manage Kubernetes workloads with ArgoCD (GitOps), deploy, monitor, and troubleshoot application syncs, resource trees, and rollouts
- Manage CDN and edge infrastructure (Cloudflare) for performance, caching, and traffic management
- Own the observability stack: Grafana (dashboards, Loki logs, Prometheus metrics) and New Relic (APM, golden metrics, transaction analysis)
- Participate in on-call rotation via PagerDuty, handle incident response, and perform root cause analysis
- Support the platform that runs AI agent workloads including job scheduling, trajectory tracking, environment provisioning, deployments, and cost attribution
- Develop Kubernetes controllers and operators to extend platform capabilities for agent orchestration
- Build internal tools, automate workflows, and integrate systems to improve team productivity
- Emergent builds autonomous coding agents that replace traditional software development by generating, testing, and deploying production applications directly from plain-language intent.
- Our systems run in production at global scale and are used to build millions of real applications.
Requirements
- 3+ years of software/platform engineering experience with production systems
- Strong proficiency in Go or Python, you write production code in at least one daily
- Experience with GitOps tooling (ArgoCD, Flux, or similar)
- You enjoy solving complex infrastructure challenges and automating away toil
- Relational databases: experience with PostgreSQL, MySQL, or similar
- Caching: experience with Redis, Memcached, or similar for application and infrastructure-level caching
- Familiarity with GCP and/or AWS: managed Kubernetes (GKE/EKS), networking, IAM, storage, and cloud-native services (SES, SQS, S3, etc.)
- Experience with CDN/edge platforms (Cloudflare, CloudFront, or similar)
- experience with PostgreSQL, MySQL, or similar
- experience with Redis, Memcached, or similar for application and infrastructure-level caching
Nice to have
- Experience tuning Kubernetes core components (API server, kubelet, scheduler)
- Familiarity with AI/LLM infrastructure: token management, cost tracking, agent orchestration
- Experience with CI/CD pipelines (GitHub Actions, automated testing, deployment pipelines)
- Infrastructure as Code experience (Terraform, Pulumi, or similar)
- Previous work on large-scale distributed systems or platform-as-a-service
- Startup experience, you thrive in fast-paced, ambiguous environments
Skills
- Benefits and Perks:
- 401(k)
- Health, dental, and vision insurance
- Unlimited Paid Time Off: take the time you need to recharge and come back refreshed
- Flexible Working Hours: work arrangements that fit your life and commitments
- Let's build the future of software together.
- Enhance monitoring, alerting, and distributed tracing across services
- Proactively identify reliability risks before they become incidents
- Work closely with product and backend teams to ensure platform scalability and reliability
- Stay current with Kubernetes releases, CNCF ecosystem updates, and cloud-native best practices
- process management, filesystem, memory, systemd, and comfortable debugging with tools like strace, tcpdump, and netstat
- Data and Messaging Infrastructure
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This listing is sourced directly from Emergent Labs's careers page and normalized into a canonical job model.