Senior Inference Optimization ML Engineer

Rhoda AI, Mountain View

The role

Running large multimodal foundation models for a growing fleet of robots is expensive. We can't rely on one hardware vendor or the most expensive accelerators. We're looking for an Inference Optimization engineer to make Rhoda's models run fast, correctly, and cheaply on a wide range of hardware: AMD Instinct GPUs, Google TPUs, wafer-scale and dataflow accelerators, consumer GPUs like the RTX 5090, and the Jetson/Thor-class compute on our robots. You'll decide which chip runs which workload, and then make sure it runs well there. You'll work closely with the research, robotics, and infrastructure teams.

What you will do

Own cost-efficient inference end-to-end. Make Rhoda's foundation models (vision, video, language, proprioception, action) faster and cheaper to run, measured in latency, throughput, and cost per inference.
Port and optimize models for non-NVIDIA and lower-cost hardware. This includes AMD (ROCm, HIP, MIOpen/hipBLASLt, Composable Kernel), TPU (XLA, JAX, Pallas, PyTorch/XLA), wafer-scale and dataflow accelerators (their SDKs and compilers), and consumer Blackwell GPUs such as the RTX 5090 (FP8/FP4, small memory, multi-GPU setups over PCIe).
Own on-robot inference. Deploy and optimize models on Jetson/Thor-class embedded compute under tight latency, power, thermal, and memory limits, and decide how work is split between the robot and the cloud to meet real-time control requirements.
Evaluate hardware rigorously. Build cost/performance models (perf/$, perf/W, memory capacity and bandwidth, interconnect) for each chip and model family. Write clear recommendations on which hardware to buy or rent for serving, offline evaluation, and on-robot use.
Build hardware-portable optimizations. Write and tune kernels that work across vendors where possible (Triton, Pallas, MLIR-based compilers) and vendor-specific ones where the speedup justifies it. Handle attention, KV cache, memory layout, and fused operators on every backend.
Apply model compression for constrained hardware. Use quantization (INT8, FP8, FP4/MX formats, AWQ/GPTQ), pruning, distillation, and model or tensor parallelism so models fit and perform well on chips with less memory or limited data-type support.
Keep results correct and consistent across backends. Build tests that check numerical parity, policy quality, and robot-relevant behaviour, so a model gives the same results whichever chip it runs on.
Build benchmarking and regression infrastructure across hardware. Track latency baselines, throughput curves, and cost per inference for every model version on every target, with automated detection of regressions.
Work with research engineers to design models that stay efficient and portable from the start, for example by avoiding operators that only exist on one vendor's hardware.

Senior Inference Optimization ML Engineer

The role

Senior Inference Optimization ML Engineer

The role

What you will do

What you need

Nice to have

Why this role

About Rhoda AI

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