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FlexAI

engineering opportunity

Senior Backend Engineer

Architect and develop high-performance Golang services for AI PaaS and infrastructure platforms. Lead backend architecture, scale platform services, and collaborate with AI/ML teams to integrate systems with training and inference pipelines.

Santa Clara, California, United StatesonsiteFULL_TIME

Posted

About the role

What will you do at FlexAI?

Role Overview FlexAI is looking for a Senior Backend Engineer (Infrastructure & AI Platform) with deep Golang expertise to architect and build the core backend systems powering our next-generation AI compute and PaaS platform. This role sits at the intersection of distributed systems, cloud infrastructure, and AI platform engineering — enabling large-scale model training, inference, and orchestration across heterogeneous compute.

This is not a traditional backend role; you will be building platform-grade systems that support AI runtimes, scheduling, resource orchestration, and multi-tenant cloud infrastructure. As a Senior Backend Engineer, you'll drive backend architecture, scale platform services, and build high-performance infrastructure components that power AI workloads in production environments — influencing how the platform evolves from Beta to enterprise-grade deployment.

Expect high ownership and technical autonomy in a research-driven, deep-tech environment — not SaaS CRUD apps. This position is In-Person and located at our San Jose, CA Office.

What You'll Do

  • Core Platform & Infrastructure Backend: Architect and develop high-performance Golang services for FlexAI's AI PaaS and infrastructure platform Build internal APIs powering model deployment, job scheduling, and compute lifecycle management Develop components interfacing with GPU/compute infrastructure and AI runtimes Distributed Systems & Scalability: Design and scale microservices and event-driven systems for high-throughput AI workloads Optimize for low latency, high concurrency, and fault tolerance Implement service-to-service communication (gRPC/REST, message queues, async pipelines) Drive reliability, observability, and resilience across services AI Platform Integration: Collaborate with AI/ML and Runtime teams to integrate systems with training pipelines, inference infrastructure, experimentation workflows, and dataset/artifact management Enable orchestration across cloud and on-prem environments Build abstractions that simplify AI infrastructure consumption Cloud-Native & Platform Engineering: Design cloud-native, Kubernetes-native services Work with DevOps/SRE on CI/CD, deployment automation, and scalability Contribute to architecture decisions for multi-region, multi-cloud infrastructure Improve monitoring, logging, and diagnostics Technical Leadership: Lead architecture reviews and set engineering standards Mentor engineers and guide complex problem-solving Drive long-term roadmap for backend infrastructure and AI platform capabilities Partner with Product, Runtime, and Infra leadership to translate requirements into scalable systems Tech Stack (Indicative): Languages: Golang (Primary), Python (Secondary) Infrastructure: Kubernetes, Docker, Cloud (AWS/GCP/Azure) Architecture: Microservices, gRPC, Event-driven systems Data: SQL + NoSQL databases, caching, streaming systems Observability: Prometheus, Grafana, OpenTelemetry (or similar) What You'll Need to Be Successful Core Engineering: 5+ years of Backend or Infrastructure Engineering experience Expert-level proficiency in Golang (must-have, heavy hands-on) Strong experience building production-grade distributed systems Proven track record on infrastructure platforms, PaaS, or deep-tech systems Infrastructure & Systems: Deep understanding of cloud-native architectures and containerized environments Strong experience with Kubernetes, Docker, and cluster orchestration Familiarity with compute scheduling, resource management, or platform runtimes is a strong plus Databases & Data Systems: Experience with distributed databases (PostgreSQL, Cassandra, DynamoDB, etc.)
  • Strong understanding of caching, queues, and streaming systems (Redis, Kafka, etc.)
  • AI / Platform Exposure (Highly Preferred): Experience on AI/ML platforms, model infrastructure, or data platforms Familiarity with ML pipelines, inference systems, or GPU-backed workloads Exposure to PyTorch, TensorFlow infrastructure, or model serving systems is a plus Ideal Candidate Profile (Who Will Thrive Here) Infra-first backend engineers (not just API developers) Background in AI infra, cloud platforms, developer platforms, or deep-tech systems Strong systems thinkers who enjoy low-level performance, scalability, and architecture challenges Startup-minded builders comfortable in ambiguous, high-ownership environments What We Offer Competitive salary and benefits package Work on cutting-edge AI infrastructure Build products used by developers and enterprises High ownership, fast execution, real impact Collaborative, high-caliber team

Which skills does this role require?

GolangDistributed systemsKubernetesDockerCloud infrastructureMicroservicesgRPCRESTPrometheusGrafanaOpenTelemetryAI PlatformPaaSDistributed SystemsCloud-nativeModel trainingOrchestrationCI/CDObservabilityFault toleranceBackend engineeringMulti-tenantPythonGoSQLREST APIsAWSGCPAzureMachine LearningProduct StrategyA/B Testing

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