API Engineering Lead

About Aldea Aldea is a multi-modal foundational AI company reimagining the scaling laws of intelligence. We believe today's architectures create unnecessary bottlenecks for the evolution of software. Our mission is to build the next generation of foundational models that power a more expressive, contextual, and intelligent human–machine interface. About the Role Aldea is looking for a highly technical API Engineering Lead to own and drive our API roadmap. You’ll lead architecture, implementation, and performance improvements across our real-time and batch speech APIs. This role sits at the intersection of backend engineering, real-time streaming, and ML productionization. What You’ll Do Own end-to-end design, architecture, and evolution of Aldea’s customer facing and backend APIs Architect for Scale & Speed: Design and evolve the global control plane for Aldea’s real-time (WebSocket/gRPC) and batch APIs. Productionize ML at the Edge: Build the high-throughput inference layer that wraps our speech models. Optimize for millisecond-level cold starts and efficient GPU utilization. Reliability & Observability: implement distributed tracing (OpenTelemetry) and define strict SLOs for streaming stability (e.g., jitter, connection drop rates). Security by Design: Lead the implementation of enterprise-grade security (API keys, OAuth2, rate limiting) and compliance controls (SOC2, data retention) for sensitive audio data. Define API standards, versioning, authentication, usage metering Partner with Research, Product, and Infra to deliver new capabilities Mentor engineers and set best practices for API engineering What You Bring 5–8 years backend/systems engineering experience Strong experience building and running production APIs Deep Proficiency in Go or Rust. (Python is great for modeling, but our hot path needs systems-level performance). Experience with streaming systems (websockets, gRPC, real-time audio/video) ML Infrastructure Experience: Familiarity with model serving (Triton, TorchServe, Ray) or orchestrating GPU workloads on Kubernetes/AWS. Deep understanding of distributed systems, performance tuning, async I/O Cloud Native Fluency: Hands-on experience with AWS (EKS/ECS, Lambda, ElastiCache). Bonus Background in speech, audio, DSP, or ML inference pipelines Experience building SDKs, developer tooling, or API billing/usage systems Early-stage startup experience Success Looks Like APIs that are fast, reliable, and developer-friendly Seamless integration of new models and features Strong engineering foundation that supports rapid iteration