Performance Modeling Engineer, AI Inference Deployments (Senior/Principal)

Posted 11 Hours Ago
Hiring Remotely in United States
Remote
220K-350K Annually
Senior level
Hardware • Information Technology • Software
The Role
Build analytical and simulation-based performance models for large-scale AI inference deployments on Positron hardware. Model parallelism, networking, batching, latency, throughput, and mixed accelerator configurations; validate projections against hardware measurements; calibrate models as systems evolve; and create customer-facing sizing and performance-estimation tools. Partner with customers, product, and chip-modeling teams to compare deployments, explain performance drivers, and communicate assumptions and confidence levels.
Summary Generated by Built In

About Positron AI
Positron AI is building next-generation AI inference accelerators designed from the ground up for low-latency, high-throughput large language model inference. Chips built for the training era keep running into two walls at inference time: memory bandwidth and chip-to-chip networking. We designed our architecture to break through both. Our first-generation Atlas systems are already in production on Oracle Cloud Infrastructure. Asimov, our first custom ASIC, takes that architecture to silicon. In simulations, Asimov delivers several times more tokens per dollar and tokens per watt than leading GPUs, at a fraction of the power. Paired in our Titan system, Asimov is built to serve models beyond 16 trillion parameters. In September 2026, we raised $875 million at a $5 billion valuation. NEA led the round, joined by Atreides Management, Valor Equity Partners, and others, to bring Asimov and Titan to market. Our team includes engineers from Groq, Lambda, NVIDIA, Meta, F5, and many other industry leaders.

Role Overview

As a Senior Performance Modeling Engineer, you will own the tools that tell customers how a model will run on Positron hardware at scale: how many chips it needs, what throughput and latency it will deliver, and how that compares with GPUs and other accelerators. You will work directly with customers, product, and our chip modeling team to turn deployment questions into quantified answers they can plan against.

The work spans analytical modeling, simulation, and real hardware measurements. You will build deployment-level models on top of our chip performance simulators, validate them against hardware, and package the results so customers can run them on their own. We are hiring two engineers for this work: one will take primary responsibility for the modeling architecture, and the other for per-model deployment studies and the customer-facing tool.

Key Responsibilities

Customer and Product Partnership

  • Partner with customers and product to turn deployment questions into focused studies with clear assumptions, options, metrics, and recommendations.
  • Run apples-to-apples comparisons of the same deployment on competitor hardware and explain what drives the difference.
  • Design and deliver a standalone performance-estimation tool that customers can run on their own workloads.

Deployment and System Modeling

  • Model deployment topologies for large-scale inference, including tensor, pipeline, expert, and data parallelism, batching, context length, and mixed Positron-plus-GPU configurations.
  • Model the network and system layer, including interconnect latency and bandwidth, redundancy, and how these bound throughput as chip count grows.
  • Develop and extend analytical and simulation-based deployment models that consume our chip performance simulator, so every estimate traces back to one shared hardware model.

Validation and Calibration

  • Validate projections against measured hardware runs and keep models calibrated as chips, software, and workloads evolve.
Required Qualifications
  • 5+ years of experience in performance modeling, capacity planning, or systems architecture for large-scale AI inference, HPC, or data-center systems.
  • Demonstrated experience building analytical or queueing performance models and calibrating them against measured systems.
  • Deep understanding of LLM inference at the systems level, including prefill versus decode, KV cache, parallelism strategies, mixture-of-experts routing, batching, time-to-first-token, and tokens per second.
  • A solid working knowledge of data-center networking (latency, bandwidth, and topology) and GPU performance characteristics.
  • Comfort working from incomplete information, stating assumptions explicitly, and delivering an answer with error bars rather than waiting for certainty.
  • Strong Rust skills, or expert-level C/C++ skills and a willingness to work in Rust day to day, along with proficiency in Python for analysis, experiments, and visualization.
  • Excellent communication skills, with the ability to explain a sizing result, what drives it, and how much to trust it to both engineers and customers.
Preferred Qualifications
  • Hands-on experience sizing or benchmarking inference deployments across many accelerators in production.
  • A track record of building customer-facing performance or TCO tools.
Leveling & Scope

While this role is currently posted at a specific level, we are a growth-oriented organization and are open to hiring at a more senior level for the right candidate. Please note that this job description serves as a focused but generalized overview of the role; specific responsibilities and impact expectations will be tailored to the experience and seniority of the final hire.

Why Join Us?
  • Your models will directly shape how customers size, plan, and buy frontier-scale inference deployments, and how Positron's hardware is positioned against GPUs and other accelerators.
  • You will work at the intersection of silicon, systems, and customers, with direct access to our chip simulators, real hardware measurements, and the engineers building Asimov and Titan.
Compensation & Benefits

The base salary range for this role for Senior: $220,000 – $270,000, for Principal: $270,000-$350,000.


Please note that the figures provided represent the base salary range only and do not include other elements of our total compensation package, equity, or comprehensive benefits.


At Positron AI, we value the unique expertise each candidate brings. While the range above reflects our typical expectation for the position, we reserve the flexibility to exceed this range for candidates whose specialized skills, significant experience, or unique qualifications fall outside the standard scope of the role. Final offers are determined based on a variety of factors, including internal equity, and individual impact.

Benefits & Perks

We want you to do your best work and feel confident that you and your family are taken care of. That means comprehensive coverage, real time to rest, and support for your future.

Health and wellness

  • Fully company-paid medical, dental, and vision insurance for you and your dependents
  • Company-paid life and disability coverage, with voluntary options to add more
  • Supplemental hospital, critical illness, and accident coverage available

Time off and flexibility

  • Unlimited paid time off; we encourage everyone to truly unplug and recharge
  • 13 paid company holidays
  • Remote-first culture with a company-provided computer and home office setup

Compensation and future

  • Competitive salary and equity
  • 401(k) with company matching, eligible from day one
Visa Support

This position is open to candidates currently authorized to work in the U.S. We cannot provide new visa sponsorship for this role but are open to facilitating H-1B visa transfers for eligible candidates.


Equal Opportunity Employer. If you're excited about the role but don't meet every bullet, we'd still love to hear from you.


Skills Required

  • 5+ years of experience in performance modeling, capacity planning, or systems architecture for large-scale AI inference, HPC, or data-center systems
  • Experience building analytical or queueing performance models and calibrating them against measured systems
  • Deep systems-level understanding of LLM inference, including prefill, decode, KV cache, parallelism, mixture-of-experts routing, batching, time-to-first-token, and tokens per second
  • Working knowledge of data-center networking, including latency, bandwidth, topology, and GPU performance characteristics
  • Ability to work with incomplete information, state assumptions explicitly, and provide estimates with error bars
  • Strong Rust skills, or expert-level C/C++ skills with willingness to work in Rust
  • Proficiency in Python for analysis, experiments, and visualization
  • Excellent communication skills for explaining sizing results, performance drivers, and confidence levels to engineers and customers
  • Hands-on experience sizing or benchmarking inference deployments across many accelerators in production
  • Experience building customer-facing performance or total-cost-of-ownership tools
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The Company
HQ: Montréal
42 Employees
Year Founded: 2023

What We Do

Positron delivers vendor freedom and faster inference for both enterprises and research teams, by allowing them to use hardware and software explicitly designed from the ground up for generative and large language models (LLMs). Through lower power usage and drastically lower total cost of ownership (TCO), Positron enables you to run popular open source LLMs to serve multiple users at high token rates and long context lengths. Positron is also designing its own ASIC to expand from inference and fine tuning to also support training and other parallel compute workloads.

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