Staff Software Engineer: Microservice Infrastructure & Real-Time ML Inference

About the role

We're looking for a Staff Software Engineer (Backend) to design and build the next generation of our real-time translation infrastructure. You'll architect mission-critical microservices that power low-latency audio/video processing pipelines, working with cutting-edge speech recognition, translation, and voice synthesis technologies. You'll be instrumental in scaling our platform to handle millions of concurrent

streaming sessions while maintaining sub-100ms latency requirements. This role combines deep systems programming, distributed systems architecture, and cloud infrastructure expertise.

Mission & Scope

Own Sanas’ microservice and streaming architecture, that power sub-100 ms, real-time language translation in both B2B and B2C environments. Define Technical Strategy, align multiple teams, and raise the bar on reliability, performance, and reliability across regions.

What you'll do

• Lead the design for high-throughput, low-latency microservices that enable bidirectional streaming in Sanas’ audio/video pipelines.
• Build event/telemetry/feature pipelines (Kafka/Redis/DynamoDB) that support near-real-time decisions and model features at scale.
• Productionize model serving (Triton/vLLM/TorchServe), implement autoscaling/batching/shadow-deploys, and enforce p99/p999 budgets.
• Establish SLOs/error budgets, graceful degradation (keep call quality first), idempotency, circuit breakers, retries with jitter, and chaos drills.
• Lead Sanas-wide logging/metrics/tracing (OpenTelemetry), RED/USE dashboards, and symptom-based alerting.
• Drive cross-team designs, mentor seniors, lead postmortems/design reviews, and lay the foundation for shared libraries and patterns (auth, interceptors, tracing, schema rollout).

Qualifications

• 7+ years of Software Engineering experience, with a focus on distributed architecture and technical leadership.
• Strong proficiency in Python or Go; strong async/concurrency (asyncio/futures), profiling, and GC/heap tuning.
• Strong proficiency in Containerization and Orchestration: AWS/Azure, Terraform, Kubernetes, IaaC patterns and node pools. (CPU/GPU)
• Experience in ML Inference: Triton/vLLM/TorchServe; GPU scheduling/packing, batching, A/B and shadow traffic.
• Experience with gRPC/protobuf at scale (versioning, interceptors, performance tuning, and compatibility testing)
• Nice-to-have: Experience with WebRTC/SRTP, RTP/RTCP, NAT traversal STUN/TURN,, SIP interop; FFmpeg/codec tradeoffs.
• Nice-to-have: Experience in data streaming with Kafka, Redis, DynamoDB; exactly-once/at-least-once patterns; stream-batch bridges.