Sample document — fictional candidate for illustration only. Where real organisations are named, the person, their role there and every result are invented; no connection with or endorsement by those organisations is implied. Your CV is prepared from your own record.
Professional Summary
Staff engineer with 10 years of experience across ML infrastructure, distributed systems, and large-scale platform architecture. At Meta, led the ground-up redesign of an ML inference pipeline now serving 80 million requests per day with 99.95% uptime. Owns three core platform services. Published researcher in distributed ML scheduling; PhD from Stanford. Known for technical clarity, cross-functional influence, and growing engineers into senior roles.
Tech Stack
Python
C++
Kubernetes
Apache Spark
PyTorch
Thrift / gRPC
Apache Flink
Presto
Hive
HDFS
Grafana
Scuba
Experience
Meta, London
- Architected and led delivery of the next-generation ML inference gateway — a Python/C++ hybrid serving 80M req/day with p99 latency under 12 ms; zero-downtime rollout over 6 months.
- Own three platform services (model registry, feature serving cache, batch scoring scheduler) across a combined on-call surface of 40+ engineers.
- Drove adoption of Kubernetes-based autoscaling for inference workers, reducing over-provisioning cost by £2.1M/year across EMEA serving clusters.
- Mentored 4 senior engineers; 2 promoted to staff level within 18 months under structured technical growth plans.
- Authored two internal RFCs — on model versioning semantics and canary evaluation standards — adopted as org-wide policy in 2024.
Twitter, San Francisco
- Built Spark-based pipeline to compute user engagement signals at scale; reduced feature freshness lag from 4 hours to 18 minutes.
- Designed schema evolution framework for Thrift-based event logs, enabling zero-downtime migrations across 200+ downstream consumers.
- Led a 5-person squad delivering real-time ad targeting feature store; contributed to 6% improvement in ad relevance scores.
Google DeepMind (intern → researcher), London
- Developed distributed task scheduler prototype in C++ for large-scale RL training jobs; results informed internal job orchestration tooling.
- Co-authored conference paper on adaptive resource allocation for ML workloads (NeurIPS 2016 workshop).
Education
PhD Computer Science — Distributed Systems
Sep 2012 – Jun 2015
Stanford University — Advisor: Prof. John Ousterhout; Thesis on fault-tolerant ML scheduling
BEng Software Engineering
Sep 2008 – Jun 2012
Tsinghua University, Beijing — Ranked 3rd in department, summa cum laude
Selected Publications & Talks
NeurIPS 2016 Workshop — "Adaptive Scheduling for Distributed ML Training" · QCon London 2023 — "Lessons from 80M req/day: ML Serving at Meta Scale" · 3 internal Meta tech talks with 400+ engineering attendees