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 •••• engineers and client scientists, and you'll own your area outright.
About ••••: •••• 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 ••••? Check out these resources in addition to the job description. Meet ••••Life at ••••Culture and Values at ••••Career Development at ••••Benefits at ••••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 •••• engineers and client scientists, and you'll own your area outright.
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 - >•••••••••••••••••••••• Important:All roles are subject to standard hiring verification practices, which may include background checks, employment verification, and other relevant checks. EEO and Accommodations: •••• 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. •••• will also consider qualified applications with criminal histories, consistent with legal requirements. •••• 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.What You Will Do:
Training platform
Distributed training
Benchmarking and evals
Your Technical Toolkit:
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 ••••.
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.