Senior AI Compute Platform Engineer

Favia; United States; Remote; USD 180,000 - 300,000; Full time

Job summary

Favia is building a dedicated AI Compute and Infrastructure team to power the next generation of model training, inference, evaluation, and experimentation across the exchange. This team sits within engineering leadership and owns the infrastructure layer that lets Favia run AI workloads with control, speed, reliability, and cost discipline.

You will join a small, senior, high-impact team working directly with AI/ML researchers, platform engineers, security teams, and product teams.

Qualifications

• 5+ years of infrastructure engineering experience with GPU compute or ML infra.
• Hands-on experience operating GPU clusters in production, including scheduling and cost optimization.
• Strong Linux, networking, storage, containers, and Kubernetes fundamentals.
• Experience with ML serving frameworks such as vLLM, Triton, TensorRT, TorchServe, KServe, Ray Serve.
• Proficiency in Python for automation, tooling, and pipelines.
• Understanding of performance tradeoffs across batching, memory usage, latency, throughput, and cost.
• Experience building observable systems with metrics, logs, traces, dashboards, and incident workflows.
• Clear communication of infrastructure tradeoffs to researchers, product teams, security, and leadership.

Responsibilities

• Own and operate GPU and accelerator clusters for training, inference, evaluation, and experimentation.
• Design infrastructure enabling local model runs on GPUs to reduce dependency on external providers and control costs.
• Build and improve scheduling, orchestration, placement, quota management, and utilization across heterogeneous accelerators.
• Optimize inference pipelines for latency and throughput using ML serving stacks.
• Partner with ML engineers to remove bottlenecks in training, evaluation, and production workflows.
• Build observability for GPU utilization, memory pressure, queue depth, latency, and spend.
• Drive reliability, incident response, runbooks, and post-incident improvements for always-on AI compute.
• Evaluate new hardware, accelerators, and serving frameworks as the landscape evolves.
• Develop tooling to make GPU usage visible and consumable by internal teams.
• Contribute to long-term architecture decisions balancing performance, cost, and scalability.

Skills

GPU compute infra

Distributed systems

Linux fundamentals

Python automation

Observability

Performance tradeoffs

Interdisciplinary collaboration

Tools

Kubernetes

Docker

CUDA

TensorRT

NCCL

TorchServe

Job description

Favia is building a dedicated AI Compute and Infrastructure team to power the next generation of model training, inference, evaluation, and experimentation across the exchange. This team sits within engineering leadership and owns the infrastructure layer that lets Favia run AI workloads with control, speed, reliability, and cost discipline.

You will join a small, senior, high-impact team working directly with AI/ML researchers, platform engineers, security teams, and product teams.