Senior Inference Engineer, AGI

Amazon; Seattle (WA); On-site; USD 167,000 - 226,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

Paid time off

Parental leave

RSUs

Job summary

Amazon in Seattle is seeking a Senior Inference Engineer to own end-to-end real-time inference for multimodal models, spanning research to production. You will shape architectures for servability, build streaming runtimes, and develop offline systems for RL and post-training rollout.

You will collaborate with scientists and hardware partners to achieve sub-second latency on real-time workloads while controlling cost and ensuring scalability across distributed systems and multiple GPUs.

Qualifications

• 3+ years building ML models for business use cases.
• PhD or MS with 6+ years of applied research.
• Experience programming in Java, C++, Python or related language.
• Hands-on experience optimizing inference for neural models and profiling.

Responsibilities

• Partner with researchers to surface latency, memory, and cost implications early.
• Implement and optimize inference paths for large multimodal models with low latency.
• Apply efficiency techniques like quantization and decoding trade-offs.
• Develop high-performance kernels and integrate into production serving.
• Profile end-to-end performance with Nsight Compute/Systems and roofline analysis.
• Own real-time streaming serving path and meet sub-second latency under load.
• Brandish continuous batching, scheduling, and preemption for throughput vs latency.
• Customize production serving frameworks for real-time streaming generative models.
• Implement multi-GPU inference and drive cost towards production baselines.
• Publish latency, throughput, and cost benchmarks to gate deployment.

Skills

ML model building

Java/C++/Python

Inference optimization

GPU performance

Production scale delivery

Education

PhD or MS + 6y

Tools

vLLM

PyTorch

TensorRT-LLM

NCCL

Job description

We are looking for a Senior Inference Engineer to own inference for real-time multimodal

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

• 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
• 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
• 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

Basic Qualifications

• 3+ years of building machine learning models for business application experience
• PhD, or Master's degree and 6+ years of applied research experience
• Experience programming in Java, C++, Python or related language
• Experience with neural deep learning methods and machine learning
• 2+ years of hands-on experience optimizing inference for neural models — not just using inference frameworks, but profiling and improving them
• Strong understanding of deep learning architectures (transformers, attention mechanisms, autoregressive decoding) and their application to speech/audio or other multimodal domains
• Production track record delivering latency-constrained, real-time inference systems under concurrent load
• Experience with GPU performance optimization — memory hierarchy, occupancy, KV-cache management, and the accelerator programming model
• Demonstrated ownership of a technical area — driving execution for a workstream and collaborating effectively across scientists and engineers

Preferred Qualifications

• Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
• Experience with production LLM/multimodal serving internals (e.g., vLLM, TensorRT-LLM): scheduler, batching, block manager, sampler customization
• Hands-on experience building real-time or streaming AI systems — speech, audio, or video — with hard latency budgets
• Experience authoring custom GPU kernels (CUTLASS, Triton, raw CUDA/PTX), fused attention (FlashAttention-style), or quantized GEMM
• Familiarity with model-compression and efficiency techniques — quantization, pruning, distillation, speculative decoding, long-context optimization
• Experience building offline inference or rollout/reward-serving infrastructure for reinforcement learning or large-scale evaluation
• Experience with distributed training and post-training pipelines (SFT through RL) — parallelism strategies, training stability, and multi-accelerator communication (NCCL, NVLink)
• Familiarity with multiple hardware backends (NVIDIA GPU, AWS Neuron/Trainium, edge accelerators) and how architecture choices affect inference latency, memory, and cost
• Background in speech-to-speech or audio generative models (codec models, autoregressive audio generation), speech recognition, or speech synthesis
• Experience shipping research to production at scale — models serving real users, not just benchmark results
• Contributions to open-source inference/kernel projects (vLLM, CUTLASS, FlashAttention, TensorRT-LLM, Triton, or similar)

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

• USA, CA, Sunnyvale - 192,200.00 - 260,000.00 USD annually
• USA, MA, Boston - 167,100.00 - 226,100.00 USD annually
• USA, WA, Seattle - 167,100.00 - 226,100.00 USD annually

Company - Amazon.com Services LLC

Job ID: A10517288