Senior Inference Engineer, AGI

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