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Positron

Memory-first AI inference silicon for LLMs

Technical Product Manager - AI Inference, Software

Technical Product ManagementPosted on Aug 12
Full-timeRemote in USARemote

About the Job

Requirements

  • At least 10 years of experience with a strong mix of technical skills, including ML systems, inference infrastructure, or serving-stack engineering with direct ownership of performance or architecture trade-offs.
  • Knowledge of transformer internals at the operator level, including attention variants, mixture-of-experts routing, KV-cache mechanics, quantization formats, and their hardware implications.
  • Experience with production inference serving at scale, including multi-tenancy, latency service-level agreements (TTFT/TPOT), batching and scheduling, disaggregated serving, KV-cache management, and observability.
  • Knowledge of the open-source inference ecosystem, including runtimes such as vLLM and SGLang, kernels, model ingestion, and how models are released, quantized, and adopted.
  • Performance-analysis skills spanning models and systems, including utilization reasoning, tokens-per-dollar and tokens-per-watt calculations, and benchmark design; able to build and defend the calculations personally.
  • Experience with competitive landscaping and analysis of inference providers and serving stacks, at a model lab, inference API provider, or AI hardware company.
  • At least 10 years with a strong mix of personal and analytical skills, including the ability to span model-architecture details through fleet-scale serving systems and stay current with the model and inference landscape.
  • Excellent communication and people skills, comfort navigating uncertainty, and ability to drive idea and decision socialization.
  • Ability to be the most technically grounded person in a go-to-market room and the most market-aware person in an engineering room.
  • Ability to own decision history by documenting why decisions were made and doing so with executive leadership.
  • Demonstrated daily, hands-on use of agentic AI in technical work, including building and running agent workflows for research, analysis, and drafting requirements, with the judgment to verify and own the output.

Responsibilities

  • Lead all aspects of AI inference and software technical product planning.
  • Write requirements for the inference software stack, spanning model coverage, numerics, inference-engine features and modes, serving-stack features and modes, and managed-service capabilities.
  • Track model and inference-system developments as an ongoing discipline and convert them into requirements before they become customer escalations.
  • Work with engineering and go-to-market teams to communicate a roadmap.
  • Work closely with go-to-market teams to understand customer and market needs.
  • Define and manage the software product lifecycle, including versions and release trains, feature modes, the model catalog, and deprecation policy.
  • Create competitive briefings.
  • Work with key ecosystem partners—including model labs, open-source runtimes, and serving and orchestration partners—to understand their technology roadmaps.
  • Create scope and feasibility frameworks to convert model and inference-system innovations into tangible engineering requests.
  • Ensure software products are well documented.
  • Streamline and automate product-planning processes using agentic AI.

About the Company

Positron

Memory-first AI inference silicon for LLMs

Positron develops memory-first AI inference silicon and systems to scale large-language-model serving. Its Atlas systems rely on commodity LPDDR5X memory rather than expensive HBM/CoWoS to boost bandwidth and capacity, enabling more efficient LLM deployment at hyperscale. The company’s next-generation Asimov silicon (tape-out planned for late 2026; production in 2027) pairs compute with very large memory per chip (roughly 288 GB to 2,304 GB) and is designed to be combined into Titan configurations (four to eight Asimov chips) for extremely large models and long context windows. Positron has already deployed Atlas systems in the cloud, including 50+ racks at Oracle Cloud Infrastructure and partnerships with Parasail, Jump Trading, and i3d.net. Its goal is to provide scalable, memory-rich AI inference hardware that lowers cost and increases throughput for serving large language models.

Company size
51-200
Company stage
Series C
Total funding
$1.2B
Headquarters
Reno, NV
Founded
2023

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