Job description

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. Meet Egen Life at Egen Culture and Values at Egen Career Development at Egen Benefits at Egen About the opportunity We're looking for an engineer who is equally at home running a GPU training platform and the large-scale model training that runs on it. You'll be embedded with a healthcare and life-sciences AI client that trains foundation models on a large GKE-based H200 and GB200 cluster. The client is reorganizing so that its foundation-model platform and its agent platform teams work more closely together. The role centres on the training stack and the scientists who use it. Solid infrastructure knowledge and experience are required, but the client already has strong SRE and infrastructure capacity, so running the cluster isn't the focus: you'll partner with that team and understand the training workloads well enough to tune, benchmark, and debug them end to end. You'll work alongside other Egen engineers and client scientists, and you'll own your area outright. What You Will Do: Training platform Work with the client's infrastructure team on the GKE training platform (GPU node pools, job queueing and scheduling, quota and capacity) from the training workload's side. Diagnose and fix platform failures at the root (for example a controller that keeps running out of memory) rather than restarting them. Tune multi-node GPU networking (RDMA/GPUDirect, NCCL) for training throughput. Distributed training Run, scale and debug multi-node distributed training with PyTorch (FSDP, DeepSpeed, Megatron or NeMo-style) and Ray Train. Profile jobs and remove bottlenecks across compute, memory, input pipelines and interconnect. Benchmarking and evals Design and run performance benchmarks for the cluster and training stack: throughput, scaling efficiency, cost per run. Build and maintain model evaluation harnesses so changes to the platform or training recipes are measured, not assumed. Instrument training and the platform with telemetry and monitoring, and keep end-to-end pipeline tests running against what users actually run. Ownership on the client team Treat the platform as your own: proactively find issues, plug gaps and propose solutions before anyone asks, keep the team informed of status and risks, and look after the scientists who use it. Explain and defend your design decisions to client engineers and scientists in detail. Stay effective while the client's priorities and team structure change. Your Technical Toolkit: Core Languages : Mastery of Python and shell scripting AI/LLM Ecosystem : Extensive experience with Google Gemini, GPT-4, or LLaMA; deep knowledge of Prompt Engineering and Fine-tuning Data & Search : Expertise in Vector Databases (Vertex AI Vector Search, pgvector, etc.) and implementing Semantic Search Infrastructure : Hands-on experience with Google Cloud (Vertex AI) and building scalable software architectures Frameworks : Proficiency in LangChain, LlamaIndex, or similar orchestration layers Mindset: A strong software engineering foundation—you write clean, maintainable code and understand the full SDLC Basic Qualifications: 5+ years of engineering experience, with recent hands-on work on GPU training infrastructure and large-scale training. Strong Python software engineering: production-quality code, tests and code review. Coding is central to this role, not an afterthought. GKE and GPU scheduling: Kubernetes on Google Cloud with GPU node pools, job queueing (Kueue or similar), and capacity and quota management. Distributed PyTorch and Ray Train: has personally run and debugged multi-node training jobs. RDMA and GPU networking: GPUDirect/RDMA, NCCL tuning, multi-node GB200 (or H200) topology. Benchmarking and evals: has designed performance benchmarks and model evaluations, and used them to make decisions. Deep enough understanding to explain how and why a system works, without leaning on AI coding tools to carry the reasoning. A track record of visible ownership: present, communicative, and accountable on a demanding team. Personal Attributes: Nice to have Healthcare or life-sciences data (genomics, clinical, biomedical), and working under HIPAA. Observability and telemetry for training platforms: metrics, tracing, alerting, end-to-end pipeline testing. Compensation & Benefits: This role is eligible for our competitive salary and comprehensive benefits package to support your well-being: - Comprehensive Health Insurance - Paid Leave (Vacation/PTO) - Paid Holidays - Sick Leave - Parental Leave - Bereavement Leave - 401 (k) Employer Match - Employee Referral Bonuses Check out our complete list of benefits here - > egen.ai Important: All roles are subject to standard hiring verification practices, which may include background checks, employment verification, and other relevant checks. EEO and Accommodations: Egen is an equal opportunity employer and is committed to inclusion, diversity, and equity in the workplace. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veterans’ status, or any other characteristic protected by federal, state, or local laws. Egen will also consider qualified applications with criminal histories, consistent with legal requirements. Egen welcomes and encourages applications from individuals with disabilities. Reasonable accommodations are available for candidates during all aspects of the selection process. Please advise the talent acquisition team if you require accommodations during the interview process.

Requirements

Must-haves

Training platform
Work with the client's infrastructure team on the GKE training platform (GPU node pools, job queueing and scheduling, quota and capacity) from the training workload's side.
Diagnose and fix platform failures at the root (for example a controller that keeps running out of memory) rather than restarting them.
Tune multi-node GPU networking (RDMA/GPUDirect, NCCL) for training throughput.
Distributed training
Run, scale and debug multi-node distributed training with PyTorch (FSDP, DeepSpeed, Megatron or NeMo-style) and Ray Train.
Profile jobs and remove bottlenecks across compute, memory, input pipelines and interconnect.
Benchmarking and evals
Design and run performance benchmarks for the cluster and training stack: throughput, scaling efficiency, cost per run.
Build and maintain model evaluation harnesses so changes to the platform or training recipes are measured, not assumed.
Instrument training and the platform with telemetry and monitoring, and keep end-to-end pipeline tests running against what users actually run.
Ownership on the client team
Treat the platform as your own: proactively find issues, plug gaps and propose solutions before anyone asks, keep the team informed of status and risks, and look after the scientists who use it.
Explain and defend your design decisions to client engineers and scientists in detail.
Stay effective while the client's priorities and team structure change.
Core Languages : Mastery of Python and shell scripting
AI/LLM Ecosystem : Extensive experience with Google Gemini, GPT-4, or LLaMA; deep knowledge of Prompt Engineering and Fine-tuning
Data & Search : Expertise in Vector Databases (Vertex AI Vector Search, pgvector, etc.) and implementing Semantic Search
Infrastructure : Hands-on experience with Google Cloud (Vertex AI) and building scalable software architectures
Frameworks : Proficiency in LangChain, LlamaIndex, or similar orchestration layers
Mindset: A strong software engineering foundation—you write clean, maintainable code and understand the full SDLC
5+ years of engineering experience, with recent hands-on work on GPU training infrastructure and large-scale training.

Nice-to-haves

Healthcare or life-sciences data (genomics, clinical, biomedical), and working under HIPAA.
Observability and telemetry for training platforms: metrics, tracing, alerting, end-to-end pipeline testing.

Application prep

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

Keywords to mirror

aipythonllmkubernetes

Platform: lever
Effort: quick

Benefits

Benefits detected in the listing.

Health Insurance

Vision

Pto

Parental Leave