{"id":2784681,"url":"https://alion.io/job/amazon-ml-software-engineer-data-plane","title":"ML Software Engineer, Data Plane","company":{"id":19,"name":"Amazon","domain":"amazon.com","url":"https://alion.io/company/amazon","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Career site","truth_index":{"grade":"A","score":87,"open_postings":1872,"ghost_share":0,"stale_share":0.733,"repost_share":0.002,"time_to_fill_p50_days":15,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Tel Aviv, Israel"],"countries":["IL"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"CI/CD","optional":false},{"name":"Linux","optional":false},{"name":"LLM","optional":false},{"name":"PyTorch","optional":false},{"name":"PyTorch C++","optional":false},{"name":"vLLM","optional":false},{"name":"JAX","optional":true},{"name":"KV Cache","optional":true},{"name":"Machine Learning","optional":true},{"name":"SGLang","optional":true},{"name":"Speculative Decoding","optional":true},{"name":"TensorRT","optional":true},{"name":"TPU","optional":true}],"status":"live","first_seen_at":"2026-08-07T00:00:00Z","employer_posted_date":"2026-10-17","last_verified_at":"2026-10-18T06:09:28Z","board_verified":true,"closed_at":null,"days_open":72,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":72},"description":"The MLIL DataPlane team is looking for a Software Development Engineer to own the design and implementation of our inference data plane. We build the software that makes large models run efficiently on custom hardware - spanning model execution, memory management, data movement, and serving integration.\nOur 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.\nThis is a ground-up effort with rapidly evolving hardware and software. We need an individual contributor 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.\nKey job responsibilities\n- Develop and optimize compute kernels for a custom ML accelerator architecture, targeting production-level performance for large language model inference.\n- Implement and validate LLM architectures end-to-end - from PyTorch model definition through distributed execution on custom hardware.\n- Integrate custom accelerator backends into open-source ML serving frameworks (vLLM, PyTorch), including scheduler extensions, memory management, and model parallelism.\n- Build and maintain test infrastructure for model correctness validation across CPU, GPU, simulator, and hardware targets.\n- Profile and optimize inference workloads - identify bottlenecks, instrument critical paths, and drive latency and throughput improvements from simulation through hardware bringup.\n- Own features end-to-end: from design through implementation, testing, and integration into the broader software stack.\n- Contribute to CI/CD pipelines that gate model and kernel changes on correctness and performance regressions.\nBasic qualifications\n- Bachelor's degree or equivalent\n- 4+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience\n- Knowledge of computer architecture, operating systems, and parallel computing\n- Strong proficiency in C/C++\n- Strong Linux systems knowledge\n- Experience developing compute kernels for GPUs, DSPs, or custom accelerators\n- Proven track record of owning and delivering complex software features end-to-end\nPreferred qualifications\n- Knowledge of ML frameworks including JAX, PyTorch, vLLM, SGLang, Dynamo, TorchXLA, and TensorRT\n- Knowledge of Machine Learning and LLM fundamentals, including transformer architecture, training/inference lifecycles, and optimization techniques\n- Experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware\n- Familiarity with speculative decoding, KV cache optimization, or other LLM serving optimizations\n- Experience with distributed systems - collective communication, RDMA, or high-speed interconnect programming\n- Demonstrated early adopter of AI-assisted development tools - uses LLMs or code-generation agents as part of daily workflow\nOur 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. 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