# Senior Inference Engineer, AGI Source: https://realanalystjobs.com/job/senior-inference-engineer-agi-amazon-96ee3bc4 Senior Inference Engineer, AGI at Amazon, an on-site role based in Boston, California, USA. Experience level: 3+ Years. Posted on 27 August 2026, read from Amazon's own careers page (Amazon), which RealAnalystJobs re-checks daily and removes the role when it closes. Amazon has approved H-1B petitions on USCIS record (employer-wide, not a promise for this role). Updated 15 November 2026. ## Key facts - Company: [Amazon](https://realanalystjobs.com/companies/amazon) - Location: Boston, California, USA - Work mode: On-site - Posted: Aug 27, 2026 - Employment type: Full time - Experience level: 3+ Years - Source: Amazon's careers page (Amazon), re-checked daily Skills: [Python](https://realanalystjobs.com/skills/python), AWS, Spark, [R](https://realanalystjobs.com/skills/r) ## Visa sponsorship The posting does not say. Amazon had H-1B petitions approved for 12,010 workers in fiscal year 2026 (USCIS). That is history, not a promise for this role. [See the record](https://realanalystjobs.com/companies/amazon#h1b-record) ## About this role 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 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 late Description from Amazon's public careers feed, reproduced so you can read the role here. Apply on the company's own site; Real Analyst Jobs never submits anything for you. ## How to apply Open [this role on Real Analyst Jobs](https://realanalystjobs.com/job/senior-inference-engineer-agi-amazon-96ee3bc4) and use Apply now there to reach the employer's application. The listing is re-checked against Amazon's careers page daily and removed when it closes. ## Amazon 583 open analyst roles on Real Analyst Jobs. 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