Job description

Senior Staff Engineer - AI ADC A10 Networks is accelerating its AI‑driven innovation across the ADC and GSLB product lines. We are seeking a highly experienced Sr. Staff Engineer to be part of the development of next‑generation AI‑aware traffic management and AI Gateway capabilities . This role combines deep domain expertise in Application Delivery Controllers (ADC), Server Load Balancing (SLB), DNS/GSLB , with practical experience in AI/LLM systems , to build intelligent routing, inference‑aware load balancing, and Layer‑8 contextual decision engines for AI workloads. The ideal candidate is a senior staff engineer who can drive architecture, design, prototyping, and execution—working closely with product management, platform teams, and AI strategy groups. 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 AI Use Guidelines for Interviews: Our interviews are designed to reflect your own skills and thinking. The use of AI or recording tools during live interviews is not permitted unless explicitly invited by the interviewer or approved in advance as part of a reasonable accommodation. If these tools are used inappropriately or in a way that misrepresents your work, your application may not move forward in the process. A10 Networks is an equal opportunity employer and a VEVRAA federal subcontractor. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, or any other characteristic protected by law. A10 also complies with all applicable state and local laws governing nondiscrimination in employment. #LI-AN1 - Hybrid Targeted compensation guideline: $180,000 - $195,000. Compensation will vary based on number of factors, including market demand for specific skills, role type, job level, and individual qualifications. Final salary offers are determined by considerations including, but not limited to, subject matter expertise, demonstrated skill level, relevant experience, geographic location, education, certifications, and training.

Requirements

Must-haves

The ideal candidate is a senior staff engineer who can drive architecture, design, prototyping, and execution—working closely with product management, platform teams, and AI strategy groups.
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.
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.

Nice-to-haves

Advanced degree in Computer Science, Networking, or related field (MS/PhD preferred).

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillmmlgopythonapi

Platform: workday
Effort: long