Machine Learning Engineer Jobs

About The Position

Requirements

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 4+ years of experience building and operating backend distributed systems end to end.
• Strong Data & ML systems fundamentals: data-intensive distributed systems, concurrency, networking and performance profiling.
• Hands-on experience with large-scale inference services on GPUs, including KV caches, prefill/decode stages and throughput/latency trade-offs.
• Direct experience with inference engines (TensorRT, vLLM, etc) or serving frameworks (Dynamo, Triton or equivalent).
• Strong programming skills in C++, Go, Rust or Python.
• Familiarity with deep learning frameworks (PyTorch, etc.) as well as model parallelism.
• Familiarity with GPU computing primitives such as CUDA, NCCL, NVLink, and hardware-specific optimizations.
• Practical understanding of high-performance networking architectures, including InfiniBand, RoCE, and low-latency cluster communication.
• Strong analytical and problem-solving skills.

Nice To Haves

• Autonomous vehicles (AV) experience is a bonus.

Responsibilities

• Own technical design and delivery of subsystems in a high-throughput, low-latency inference platform capable of handling multi-tenant, enterprise-grade inference workloads.
• 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 contracts and customer adoption.
• Decompose ambiguous work, drive issues to closure, and raise the engineering bar through code quality, reviews, testing, and mentoring.