Job description

At F5, we strive to bring a better digital world to life. Our teams empower organizations across the globe to create, secure, and run applications that enhance how we experience our evolving digital world. We are passionate about cybersecurity, from protecting consumers from fraud to enabling companies to focus on innovation. Everything we do centers around people. That means we obsess over how to make the lives of our customers, and their customers, better. And it means we prioritize a diverse F5 community where each individual can thrive. Job Description The AI Inference Engineer plays a critical role in the AI lifecycle by bridging the gap between high-performance model development and optimized deployment environments. This position focuses on optimizing Large Language Models (LLMs) for inference, serving diverse environments—from GPU-rich data centers to resource-constrained edge devices—with a strong emphasis on maximizing throughput, minimizing latency, and maintaining model accuracy. This role is pivotal in advancing F5’s AI capabilities, ensuring enterprise-grade reliability by leveraging hardware acceleration, designing scalable infrastructure, and monitoring system performance. Key Responsibilities High-Performance AI Serving Build and maintain robust inference engines using tools like vLLM , TGI (Text Generation Inference) , and NVIDIA Triton , ensuring high performance at scale. Handle deployment optimizations to deliver low-latency AI serving solutions for multiple business applications. Hardware Acceleration and Optimization Profile and optimize models for specialized hardware backends, including NVIDIA GPUs (CUDA/TensorRT), Apple Silicon (CoreML) , and AI accelerators like TPUs and LPUs . Collaborate with hardware teams to maximize utilization and performance across various computational environments. Inference Orchestration and Scalability Design and implement auto-scaling architectures for online (real-time) and batch inference pipelines, leveraging Kubernetes for inference routing and orchestration. Ensure software solutions are optimized for peak performance during traffic spikes, maintaining reliability and scalability. Performance Monitoring and Observability Establish robust observability frameworks to monitor Time to First Token (TTFT) , tokens per second, and memory bandwidth utilization against service-level agreements (SLAs). Build and execute performance and load testing suites to identify bottlenecks and ensure consistent reliability at scale. Technical Requirements Required Skills: Programming Languages: Proficiency in programming languages such as Python , C , Rust , or Golang specifically for high-performance AI workflows. Inference Tools: Proven hands-on experience with tools like vLLM , TensorRT , Llama.cpp , and Ollama for inference development and optimization. Infrastructure Expertise: Strong familiarity with infrastructure technologies, including Docker , Kubernetes , and cloud platforms such as AWS , GCP , and Azure . Hardware Optimization Expertise: Comprehensive understanding of GPU and AI hardware, including techniques for profiling and optimizing performance for accelerators like NVIDIA GPUs and TPUs. Preferred Experience: Prior experience deploying Large Language Models (LLMs) with advanced techniques like Speculative Decoding or PagedAttention . Contributions to open-source inference libraries or hardware-level kernel development (e.g., CUDA, Triton kernels). Background in MLOps or SRE roles focused on high-performance AI endpoints and reliability during demand surges. Proficiency in designing scalable solutions for high-throughput inference environments optimized for traffic bursts. Success Metrics (KPIs): Latency Reduction: Continuously improve inference latency metrics, ensuring minimal Time to First Token (TTFT) and maximum tokens per second. Cost Efficiency: Achieve lower Cost per 1K Tokens through better resource utilization and hardware optimization. Scalability: Maintain system stability and reliability during traffic spikes , ensuring performance consistency across environments. Throughput Maximization: Deploy models optimized for peak hardware usage and maximized process throughput. Why Join F5? F5 empowers you to push boundaries in AI optimization and high-performance engineering . Joining our team means: Collaborating with cutting-edge technologies and hardware solutions to support real-time AI applications. Advancing your career in a fast-paced, multidisciplinary environment focused on innovation, scalability, and problem-solving. Driving transformative projects that deliver real-time AI reliability to global customers while maintaining cost and efficiency standards. Working on advanced MLOps solutions that seamlessly scale enterprise AI systems and shape the future of intelligent deployment. What Success Looks Like: As an AI Inference Engineer at F5, success is measured by your ability to: Combine technical expertise and problem-solving skills to deliver low-latency, scalable, and high-performing AI prediction systems. Collaborate efficiently across cross-functional teams, participating in knowledge sharing and system refinement. Demonstrate initiative by driving optimizations across hardware, tools, and orchestration processes, balancing immediate solutions with long-term architectural goals. Translate complex AI and inference workflows into practical solutions that align with F5 s strategic objectives. The base pay range per annum for this position is: $176,600 - $265,000 F5 maintains broad salary ranges for its roles in order to account for variations in knowledge, skills, experience, geographic locations, and market conditions, as well as to reflect F5’s differing products, industries, and lines of business. The pay range referenced is as of the time of the job posting and is subject to change. You may also be offered incentive compensation, bonus, restricted stock units, and benefits. More details about F5’s benefits can be found at the following link: f5.com. F5 reserves the right to change or terminate any benefit plan without notice. #LI-ZB1 The Job Description is intended to be a general representation of the responsibilities and requirements of the job. However, the description may not be all-inclusive, and responsibilities and requirements are subject to change. Please note that F5 only contacts candidates through F5 email address (ending with f5.com) or auto email notification from Workday (ending with f5.com or myworkday.com ) . Equal Employment Opportunity It is the policy of F5 to provide equal employment opportunities to all employees and employment applicants without regard to unlawful considerations of race, religion, color, national origin, sex, sexual orientation, gender identity or expression, age, sensory, physical, or mental disability, marital status, veteran or military status, genetic information, or any other classification protected by applicable local, state, or federal laws. This policy applies to all aspects of employment, including, but not limited to, hiring, job assignment, compensation, promotion, benefits, training, discipline, and termination. F5 offers a variety of reasonable accommodations for candidates . Requesting an accommodation is completely voluntary. F5 will assess the need for accommodations in the application process separately from those that may be needed to perform the job. Request by contacting accommodations f5.com .

Requirements

Must-haves

Everything we do centers around people. That means we obsess over how to make the lives of our customers, and their customers, better. And it means we prioritize a diverse F5 community where each individual can thrive.
This role is pivotal in advancing F5’s AI capabilities, ensuring enterprise-grade reliability by leveraging hardware acceleration, designing scalable infrastructure, and monitoring system performance.
High-Performance AI Serving
Build and maintain robust inference engines using tools like vLLM , TGI (Text Generation Inference) , and NVIDIA Triton , ensuring high performance at scale.
Handle deployment optimizations to deliver low-latency AI serving solutions for multiple business applications.
Hardware Acceleration and Optimization
Profile and optimize models for specialized hardware backends, including NVIDIA GPUs (CUDA/TensorRT), Apple Silicon (CoreML) , and AI accelerators like TPUs and LPUs .
Collaborate with hardware teams to maximize utilization and performance across various computational environments.
Inference Orchestration and Scalability
Design and implement auto-scaling architectures for online (real-time) and batch inference pipelines, leveraging Kubernetes for inference routing and orchestration.
Ensure software solutions are optimized for peak performance during traffic spikes, maintaining reliability and scalability.
Performance Monitoring and Observability
Establish robust observability frameworks to monitor Time to First Token (TTFT) , tokens per second, and memory bandwidth utilization against service-level agreements (SLAs).
Build and execute performance and load testing suites to identify bottlenecks and ensure consistent reliability at scale.
Technical Requirements
Programming Languages: Proficiency in programming languages such as Python , C , Rust , or Golang specifically for high-performance AI workflows.
Inference Tools: Proven hands-on experience with tools like vLLM , TensorRT , Llama.cpp , and Ollama for inference development and optimization.
Infrastructure Expertise: Strong familiarity with infrastructure technologies, including Docker , Kubernetes , and cloud platforms such as AWS , GCP , and Azure .
Hardware Optimization Expertise: Comprehensive understanding of GPU and AI hardware, including techniques for profiling and optimizing performance for accelerators like NVIDIA GPUs and TPUs.
Latency Reduction: Continuously improve inference latency metrics, ensuring minimal Time to First Token (TTFT) and maximum tokens per second.

Nice-to-haves

Prior experience deploying Large Language Models (LLMs) with advanced techniques like Speculative Decoding or PagedAttention .
Contributions to open-source inference libraries or hardware-level kernel development (e.g., CUDA, Triton kernels).
Background in MLOps or SRE roles focused on high-performance AI endpoints and reliability during demand surges.
Proficiency in designing scalable solutions for high-throughput inference environments optimized for traffic bursts.

Application prep

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

Keywords to mirror

aikubernetespythonrustgolangdockerawsgcpazure

Platform: workday
Effort: long