Jobs and Careers

Lead Machine Learning Engineer, Inference & Performance

UKRemotefull_timeVerifiedPosted 1 Jul 2026

About the role

About Egen:

Egen is a fast-growing and entrepreneurial company with a data-first mindset. We bring together the best engineering talent working with the most advanced technology platforms, including Google Cloud and Salesforce, to help clients drive action and impact through data and insights. We are committed to being a place where the best people choose to work so they can apply their engineering and technology expertise to envision what is next for how data and platforms can change the world for the better. We are dedicated to learning, thrive on solving tough problems, and continually innovate to achieve fast, effective results. If this describes you, we want you on our team.

Want to learn more about life at Egen? Check out these resources in addition to the job description.

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About the opportunity:

As a Senior AI Engineer, you will be at the forefront of our Generative AI initiatives. We treat AI as a software engineering discipline. You will be responsible for the full lifecycle of our AI features—specifically document intelligence and RAG pipelines—taking them from initial prototype to robust, scalable production services. You will solve for real-world constraints like latency, error handling, and cost optimization.

You’ll collaborate with a diverse range of clients to translate business needs into high-performance AI architectures. This role requires a blend of deep technical expertise in LLMs and a disciplined Software Engineering approach to ensure our solutions are robust, ethical, and scalable.

What You Will Do:

Optimize Inference: Build and tune production LLM serving with vLLM and SGLang—maximizing throughput and minimizing latency through batching, paged attention, quantization, and KV-cache strategies

Profile & Accelerate Training: Instrument and profile training runs to find bottlenecks, then resolve them with the right attention implementations (e.g. FlashAttention) tuned to the underlying hardware (H200, GB200)

Engineer for the Hardware: Apply a working understanding of GPU architecture and attention internals to choose the right approach per accelerator, rather than relying on defaults

Serve at Scale: Deploy and operate multiple models within shared GPU clusters on GKE, with autoscaling, efficient bin-packing, and graceful handling of mixed workloads

Drive Efficiency: Own GPU utilization as a first-class metric—measure it, improve throughput-per-dollar, and continuously raise the ceiling on what our fleet can deliver

Collaborate & Consult: Work directly with clients to understand performance, latency, and cost requirements, and translate them into pragmatic serving and training architectures

Your Technical Toolkit:

Core Languages: Mastery of Python and shell scripting; comfort reading and reasoning about lower-level (CUDA-adjacent) performance code is a strong plus
Inference Frameworks: Hands-on experience with vLLM, SGLash, or comparable high-performance serving stacks
GPU & Model Internals: Solid grasp of GPU architecture, the fundamentals of LLM inference, and the attention mechanism—including where the bottlenecks live and how FlashAttention and similar techniques address them across hardware generations (H200, GB200)
Profiling: Fluency with profiling tools to diagnose training and inference bottlenecks (compute-bound vs. memory-bound, kernel-level analysis)
Infrastructure: Strong Kubernetes (GKE) experience—deploying and autoscaling multiple models on shared GPU clusters on Google Cloud
Mindset: A strong software engineering foundation—you write clean, maintainable code, measure before optimizing, and understand the full SDLC