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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 deploying in 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
We're looking for an exceptional Principal ASIC Architect to help define the future of AI inference hardware. You will collaborate across architecture, software, machine learning, systems, and silicon implementation to design next-generation AI accelerators capable of serving rapidly evolving frontier models. This role requires exceptional technical breadth, balancing mathematical understanding, ML trends, systems architecture, and practical silicon implementation. This may be the single most important technical hire after the Chief Architect.
Key Responsibilities
Architectural Exploration* Own architectural exploration across the evolving landscape of transformer models, MoE, sparse models, and long-context inference.
- Evaluate emerging techniques including speculative decoding, continuous batching, KV-cache evolution, sub-quadratic and linear attention, and state-space models.
- Assess memory architectures, communication architectures, and interconnects for next-generation accelerators.
Performance Modeling* Develop analytical models and build simulation frameworks to evaluate architectural tradeoffs.
- Estimate latency, throughput, bandwidth, memory and compute utilization, scaling efficiency, power, area, and cost.
Hardware/Software Co-Design* Partner closely with compiler, runtime, system software, firmware, ASIC engineering, and ML research teams to align hardware direction with real workload needs.
ASIC Specification & Roadmap* Develop architecture documents and define subsystem interfaces.
- Evaluate tradeoffs across competing architectural directions and guide the future product roadmap.
Industry Awareness* Track new LLMs, reasoning models, research papers, hardware competitors, academic research, and emerging algorithms to keep Positron's architecture ahead of the field.
AI-Enabled Engineering* Use AI to accelerate research, performance analysis, architecture exploration, simulation generation, and documentation.
Required Qualifications
- 15+ years of experience in ASIC architecture.
- Deep expertise in AI accelerator design and high-performance SoCs.
- Hands-on experience building performance models for memory systems and interconnects.
- Strong hardware/software co-design ability.
- Excellent written and verbal communication skills.
Preferred Qualifications
- Experience with GPU architecture.
- A solid grasp of ML systems, distributed inference, and large-scale serving.
- Deep knowledge of transformer internals.
- Compiler experience.
- A track record of research publications.
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.
What Success Looks Like
Success in this role looks like helping define future Positron architectures and creating a scalable architectural direction that bridges software and hardware, influencing products that remain competitive for years despite rapidly evolving AI models.
Why Join Us?
- Shape the core architecture of Positron's AI inference accelerators at a foundational level, with outsized influence over the company's technical direction for years to come.
- Work at the intersection of frontier AI research and silicon implementation, tackling problems few architects in the industry get to own end to end.
Compensation & Benefits
The base salary range for this role is $300,000 – $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, so you can 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.
Salary Context
This $300K-$350K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2094 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 10,827 AI roles we're tracking, AI/ML Engineer positions make up 77% of the market. At Positron AI, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,160 based on 13,809 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($325K) sits 53% above the category median. Disclosed range: $300K to $350K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,300; VP: $236,450.
Positron AI AI Hiring
Positron AI has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $350K - $350K.
Remote Work Context
Remote AI roles pay a median of $188,200 across 2,431 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 10,827 open positions tracked in our dataset. By seniority: 122 entry-level, 5,256 mid-level, 3,922 senior, and 1,527 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (665 positions). The remaining 10,097 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 10,827 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,288), AI Software Engineer (548), AI Product Manager (471). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (122) are outnumbered by mid-level (5,256) and senior (3,922) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,527 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (665 positions), with 10,097 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,111, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,093 postings), Aws (1,100 postings), Azure (910 postings), Rag (863 postings), Gcp (680 postings), Pytorch (549 postings), Claude (532 postings), Kubernetes (511 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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