Stack AV
Technology
StaffSoftwareEngineer,MachineLearningInferencePlatform
Neural analysis suggests this role is
optimal for Senior candidates.
“Staff Software Engineer, Machine Learning Inference Platform at Stack AV. Skills: ML inference platform, Distributed systems, GPU computing. Define platform architecture. Drive architecture”
Industry & Context.
What They're Looking For.
Must Have
7+ years backend distributed systems, Cross-team technical leadership, Data-intensive distributed systems, Concurrency, Networking, Performance profiling, Large-scale inference services on GPUs, KV caches, Prefill/decode stages, Throughput/latency trade-offs
Nice to Have
Autonomous vehicles (AV) experience
What You'll Do.
Define platform architecture
Develop developer SDKs
Optimize inference performance
Partner with product teams
Partner with infrastructure teams
Promote engineering excellence
Set culture of excellence
How You'll Work.
Team & Collaboration
Cross-team technical leadership; Partner with product; Partner with infrastructure
Communication Scope
Convey complex concepts
Full Job Description
About Stack: Stack is developing revolutionary AI and advanced autonomous systems designed to enhance safety, reliability, and efficiency of modern operations. Stack's autonomous technology incorporates cutting-edge advancements in artificial intelligence, robotics, machine learning, and cloud technologies, empowering us to create innovative solutions that address the needs and challenges of the dynamic trucking transportation industry. With decades of experience creating and deploying real world systems for demanding environments, the Stack team is dedicated to developing an autonomous solution ecosystem tailored to the trucking industry's unique demands. About the Role: In the Staff Engineer role, you will define and drive architecture for a high-throughput, low-latency, multi-tenant ML inference platform. You will balance hands-on coding with long-term technical direction, operate across ML Platform, infrastructure, MLE, and external-facing API needs, and establish principled architecture for serving, control plane, observability, capacity, tenant isolation, system economics, and model-engine integration. Responsibilities: Design platform architecture for multi-tenant inference workloads across serving, orchestration, control plane, APIs, SDKs, observability, and model-engine integration. Develop robust API layers (gRPC, WebSockets, REST, etc.) and developer SDKs that abstract complex distributed inference orchestration into seamless, reliable token streams. Build and harden a multi-tenant control plane to enable accurate metering, rate limiting, quotas, tenant isolation and noisy-neighbor fairness across the platform. Optimize inference performance across the entire system stack, including the model engine layer. Build observability and SLOs to gain insights into system economics, cache-hit rates, GPU utilization and cost accounting per model and per tenant. Partner with product and infrastructure teams on model onboarding, capacity planning, external API contra
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