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Positron

Memory-first AI inference silicon for LLMs

AI Forward Deployed Engineer Lead

AI Application EngineeringUpdated on Nov 14
Full-timeRemote in USA

About the Job

Requirements

  • A strong software engineering background is required; systems or infrastructure experience is strongly preferred.
  • Hands-on expert experience using agentic development tools such as OpenAI Codex and Claude Code, integrating large language model application programming interfaces, and building evaluations is required.
  • A shipped track record of internal tooling or developer-productivity work that engineers adopted is required.
  • The candidate must be able to build credibility with skeptical senior systems engineers through results rather than demos.
  • The candidate must be self-directed and comfortable defining a roadmap from observed friction without a team around them.
  • Excellent written communication skills are required for written plans, design documents, and asynchronous updates.
  • A low-level or hardware-adjacent background is preferred, sufficient to pair credibly on a kernel driver or trace-analysis session.
  • Experience with continuous integration and continuous delivery systems, developer platforms, or build and release engineering is preferred.
  • Prior forward-deployed, solutions-engineering, or platform-team experience is preferred.
  • Candidates must currently be authorized to work in the United States.

Responsibilities

  • Embed with Accelerator Platform Software sub-teams across firmware, runtime, performance, and simulation, and build agentic workflows into coding, debugging, triage, test generation, and documentation processes.
  • Pair directly with senior engineers on their work and earn adoption by measurably improving their workflows.
  • Own the shared skills repository and internal agent tooling that turn team knowledge into reusable, evaluated automation.
  • Integrate agents into continuous integration and everyday engineering workflows, including code review, log and trace triage, and silicon-debug assistance.
  • Build and maintain evaluations that measure workflow quality, and regression-test automation as a production system.
  • Set standards for effective AI-assisted engineering where correctness is critical, and champion AI-native practices across the team.
  • Identify where AI provides real leverage and communicate where it does not.

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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