Senior Staff Engineer - AI ADC
A10 Networks, Inc · San Francisco Bay Area · 1 wk ago
HybridEngineering$180k–$195k/yrFull-time
Key Responsibilities
- Lead architecture and development of AI Gateway components for intelligent routing of LLM/AI application traffic.
- Design and implement LLM-aware load balancing, incorporating semantic, token, latency, and model-level insights into traffic decisions.
- Develop enhancements for ADC and GSLB platforms to support AI workloads, including:
- Token-aware rate limiting
- Inference latency-based routing
- AI model endpoint discovery and health checks
- Integration with vector databases, model registries, and ML observability systems
- Contribute to advanced features including Layer-8 context-aware routing, adaptive traffic shaping, and AI-driven anomaly detection.
- Drive system-wide architecture: data plane, control plane, configuration, and distributed state management.
- Mentor engineering teams on AI workload behaviors, traffic characteristics, and tuning strategies.
- Collaborate with AI research and platform teams to align architectural decisions with product roadmap.
- Create guidance on performance optimization, benchmarking, and scaling for global multi-node deployments.
- Ensure high standards for reliability, security, observability, and cloud-native deployment models.
Required Qualifications
- Advanced degree in Computer Science, Networking, or related field (MS/PhD preferred).
- Deep knowledge of L4-L7 protocols (TCP/TLS/HTTP/2/3, QUIC), load balancing algorithms and GSLB strategies (DNS-based, HTTP redirect, anycast).
- Strong programming skills in C/C++ (data plane), Go/Python (control/ML), and performance profiling (perf, eBPF, flamegraphs, VTune/nvprof).
- Applied ML: anomaly detection, time-series forecasting, classification; experience with PyTorch/TensorFlow.
- Hands-on experience with AI/ML systems, including:
- Model inference pipelines, LLM API integrations
- Token-level behavior and performance characteristics
- Understanding of AI workload routing challenges (latency, caching, batching, multi-model orchestration)
- Strong architectural and design skills; able to lead complex technical initiatives end-to-end.
- Demonstrated ability to influence cross-functional teams and drive consensus.
Key Attributes
- Passion for emerging AI technologies and applying them within network infrastructure.
- Deep systems thinking and ability to design for performance, scale, and robustness.
- Strong ownership mindset with ability to deliver impactful architectural outcomes.
- Excellent communication and collaboration skills.
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