Sobre este puesto de Member of Technical Staff | Inference Platform en Avra
About the role
At Avra, every technical IC is a Member of Technical Staff (MTS). The title doesn't put anyone in a silo: you own systems and outcomes, not steps in a function, and you keep building depth in your area. Seniority shows up in your scope, level, and compensation, not in titles.
In this role, you'll join the Platform team to own where our models execute. Customers consume our models through large batches of millions of records and through real-time APIs, and they make business decisions on every response. You'll run governed model releases reliably and efficiently — in our cloud and on customer-hosted Kubernetes — and make inference fast, predictable, and cheap enough to serve both enterprise and mid-market customers.
What you'll do
Evolve Sophos, our online and batch inference runtime, built on Kubernetes.
Run large batch inference on ephemeral jobs, with multi-dimensional admission control (CPU, memory, GPU) through Kueue.
Build and extend the Sophos controller and its Kubernetes custom resources.
Optimize each model's inference engine and feature processing, using vectorized, columnar operations.
Serve graphs and data efficiently from Lance-based storage.
Own execution of training, post-training, and fine-tuning jobs, in our cloud and in customer dataplanes.
Drive autoscaling, GPU serving, performance, and cost optimization, with telemetry for every model we run.
Solve open problems such as deterministic job sizing, checkpointing and recovery for batch runs, per-customer encryption and isolation, resilience to difficult input files, and automatic profiling when a new model is accepted.
How we measure success
99.9% serving availability.
p95/p99 latency for online inference and throughput for batch.
Cost per prediction and per training job.
GPU utilization: paid capacity versus capacity actually used.
Training and batch jobs that finish on time and succeed without manual retries.
What we're looking for
Experience running model serving or large-scale batch compute on Kubernetes.
Experience building Kubernetes controllers or operators.
Skill at profiling and optimizing data-heavy Python pipelines.
A clear sense of cost: you treat compute efficiency as a product feature.
Production-quality code and reviews, and a willingness to operate what you build.
Nice to have
Ray, Ray Serve, or KubeRay in production.
Kueue or other batch scheduling and admission-control systems.
GPU serving and performance optimization.
Arrow, Parquet, Lance, or other columnar formats.
Shipping software to customer-hosted Kubernetes.
GCP/AWS and GKE/EKS, and financial services or regulated environments.
