We are looking for a Senior Inference Engineer to own inference for real-time multimodal
conversational AI. This is a full-stack inference role: you will work across the entire path a model
takes from research to production — shaping model architecture so it is servable, building the
real-time runtime that serves it within hard latency budgets, and building the offline systems
that train and reinforce it.
You will operate at the boundary of Science and Inference, taking frontier-scale speech and
audio models and making them run within real-time latency budgets on production hardware.
You will co-design architectures with scientists to make them inference-friendly from inception,
own the low-latency streaming serving path, and build the training and reinforcement-learning
infrastructure that closes the loop. You will have the compute, data, and runway to solve
problems that few teams in the world are positioned to tackle.
As a Senior Engineer, you will own a significant area of the inference stack end to end, drive its
technical execution, contribute to the team's roadmap, and work closely with scientists and
hardware partners to ensure our models run fast enough to feel human in real time — and at a
cost that makes them viable at scale. You may go deep in one of the areas below while
contributing across the others.
Key job responsibilities
Model Architecture & Inference Co-Design
• Partner with research scientists to make model architectures servable from inception —
surfacing the latency, memory, and cost implications of architecture choices before they are
locked in
• Implement and optimize the inference path for large-scale multimodal models — attention
and KV-cache mechanisms, multimodal/autoregressive decoding, and the compute
primitives on the critical path
Apply efficiency techniques across the stack — quantization (per-tensor/per-channel/per-
group, INT8/FP8/BF16), speculative decoding, operator fusion, and paged KV-cache — and
quantify their quality/latency trade-offs
• Develop and tune high-performance kernels for critical operations where off-the-shelf
implementations leave performance on the table, integrating them into production serving
with minimal overhead
• Profile end-to-end performance with tools such as Nsight Compute/Systems and roofline
analysis to identify and eliminate bottlenecks in large-scale inference workloads
Real-Time & Interactive Runtime
• Own the real-time serving path for streaming multimodal conversational AI, meeting sub-
second, streaming latency budgets under concurrent session load
• Build and tune continuous batching, scheduling, and preemption to balance throughput
against per-request latency SLAs for interactive workloads
• Customize production serving frameworks (e.g., vLLM, PyTorch) for real-time streaming
generative models that fall outside standard LLM serving patterns — sustained low-latency
output under concurrent session load
• Implement multi-GPU inference (tensor parallelism, collective communication) for latency-
critical paths, and drive cost toward parity with existing production baselines
• Establish latency, throughput, and cost benchmarking, and publish the operational metrics
that gate deployment
Offline Systems: Training, RL & Evaluation Infrastructure
• Build and scale the offline inference systems behind post-training — high-throughput rollout
generation and reward-model serving for reinforcement learning (RL/RLHF/RLAIF)
• Ensure train/serve consistency — that the inference path used in RL and evaluation
faithfully matches production online behavior (e.g., parity across sampling and logit
processing)
• Work with the evaluation team to enable offline inference that captures the quality
dimensions unique to real-time conversation — latency sensitivity, audio quality, and
interaction naturalness
