Platform Engineer

ABOUT EMA

Ema builds AI Employees for HR, IT and Finance. Our AI Employees take on the busy work across the employee experience, from recruiting, onboarding and benefits to IT support, invoice processing and payroll, so people can spend their time on work that needs them. Founded by former executives from Google, Coinbase, Flipkart, and Okta, our team includes engineers from premier tech companies and graduates of Stanford, MIT, UC Berkeley, CMU, and IITs.

We are backed by industry leading investors including Accel, Naspers/Prosus, Creaegis, Section32, and angels like Sheryl Sandberg and Dustin Moskovitz. Headquartered in Silicon Valley and with offices in London, Bangalore and Vancouver, Ema is at the frontier of what Agentic AI can do in production. We ship real systems that run real business processes at scale.

Who you are

You are an experienced Platform Engineer who owns backend infrastructure end to end. You design multi-tenant, microservices-based systems that other engineering teams build on, and you make deliberate architectural tradeoffs around consistency, latency, scale, and cost. You are comfortable going deep — service mesh internals, database internals, distributed-systems failure modes — and equally comfortable defining the reliability and security contracts an enterprise AI platform depends on.

Responsibilities

• Design, own, and evolve scalable microservices architectures on Kubernetes across GCP, Azure, and AWS, including multi-tenant isolation (namespaces, network policies, per-tenant resource quotas and RBAC).

• Build core platform and data-plane components in Golang and Python — data ingestion, knowledge-base indexing and vector/graph search, application connectivity, workflow automation, and ML operations — against explicit latency and throughput SLOs.

• Own service-to-service communication: gRPC/protobuf API contracts, service mesh (Istio/Linkerd), load balancing, retries, timeouts, and circuit breaking.

• Make and document architectural tradeoffs — partitioning/sharding strategy, consistency models (strong vs. eventual), caching tiers, and build-vs-buy decisions.

• Define the reliability contract: SLIs/SLOs, error budgets, capacity planning, autoscaling (HPA/VPA/KEDA), and graceful degradation.

• Design and operate the observability stack — Prometheus, Grafana, OpenTelemetry, distributed tracing, and real-time alerting — for full visibility into system health.

• Drive DevOps and platform-engineering practices: IaC (Terraform), Helm, GitOps (ArgoCD/Flux), and CI/CD pipelines.

• Optimize for performance and cost — profiling, load testing, latency budgets, and cost-per-request.

• Participate in on-call rotations and lead incident response and root-cause analysis.

Qualifications

• Bachelor's degree in Computer Science or a related field.

• 5+ years of experience in Platform, Infrastructure, or Backend Engineering.

• Strong CS fundamentals: data structures, algorithms, operating systems, and networking.

• Proficiency in Golang and Python.

• Production experience with Docker, Kubernetes, and microservices architecture.

• Hands-on experience with at least one major cloud provider (GCP, Azure, or AWS); multi-cloud a strong plus.

• Strong database expertise: query and read/write-path optimization, partitioning/sharding, replication and consistency models, with practical experience in NoSQL and graph stores. Solid grasp of the CAP theorem and database internals.

• Solid distributed-systems foundation: idempotency, backpressure, delivery semantics (at-least-once vs. exactly-once), and message queues (Kafka/Pulsar/NATS/PubSub).

• Track record of building platforms from the ground up that other engineering teams successfully build on.

Bonus

• Experience operating systems at high scale (high QPS, large data volumes).

• Depth in auth and security: secrets management (Vault), mTLS, RBAC, OIDC/SAML, network policy.

• Experience with vector databases (pgvector/Pinecone/Milvus) and graph databases (Neo4j/Neptune).

• Open-source contributions to infrastructure projects (e.g., Kubernetes operators).

Compensation offered will be determined by factors such as location, level, job-related knowledge, skills, and experience. Certain roles may be eligible for variable compensation, equity, and benefits.