Senior ML Software Engineer, Data Plane
AI in this role
Designs and implements low-level inference data plane software to run large language models efficiently on custom hardware.
Our work covers the full inference path: integrating serving engines with custom hardware, developing high-performance compute kernels, enabling efficient data movement, and driving models from early validation through production. We operate at frontier scale with large distributed models.
This is a ground-up effort with rapidly evolving hardware and software. We need a senior IC who can write and optimize low-level code for custom hardware, validate model architectures end-to-end, build test and profiling infrastructure, and drive performance across the stack.
Key job responsibilities
- Develop and optimize compute kernels for a custom ML accelerator architecture, targeting production-level performance for large language model inference.
- Implement and validate LLM architectures (decoder-only, mixture-of-experts) end-to-end - from PyTorch model definition through distributed execution on custom hardware.
- Integrate custom accelerator backends into open-source ML serving frameworks (vLLM, PyTorch), including scheduler extensions, memory management, and model parallelism.
- Build and maintain test infrastructure for model correctness validation across CPU, GPU, simulator, and hardware targets.
- Profile and optimize inference workloads - identify bottlenecks, instrument critical paths, and drive latency and throughput improvements from simulation through hardware bringup.
- Own features end-to-end: from design through implementation, testing, and integration into the broader software stack.
- Contribute to CI/CD pipelines that gate model and kernel changes on correctness and performance regressions.
- Mentor engineers, drive design reviews, and raise the engineering bar across the team.
Basic qualifications
- Bachelor's degree in computer science or equivalent
- 7+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Knowledge of Machine Learning and LLM fundamentals, including transformer architecture, training/inference lifecycles, and optimization techniques
- Knowledge of computer architecture, operating systems, and parallel computing
- Strong proficiency in C/C++
- Strong Linux systems knowledge
- Experience developing compute kernels for GPUs, DSPs, or custom accelerators
- Proven track record of owning and delivering complex software features end-to-end
Preferred qualifications
- Knowledge of ML frameworks including JAX, PyTorch, vLLM, SGLang, Dynamo, TorchXLA, and TensorRT
- Experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware, or experience with CUDA kernels or ML/low-level kernels
- Familiarity with speculative decoding, KV cache optimization, or other LLM serving optimizations
- Experience with distributed systems - collective communication, RDMA, or high-speed interconnect programming
- Experience with hardware simulation environments and model validation workflows
- Demonstrated early adopter of AI-assisted development tools - uses LLMs or code-generation agents as part of daily workflow
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How we rate this
Senior ML Software Engineer, Data Plane at Amazon rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- Tell me about a project where machine learning was part of your work. What did you do?
- Tell me about a project where deep learning was part of your work. What did you do?
- Tell me about a project where inference was part of your work. What did you do?
- Tell me about a project where kernel optimization was part of your work. What did you do?
- Walk me through how you've used vLLM in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: Machine Learning, Deep Learning, Inference, Kernel Optimization, and vLLM. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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