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About This Role
Description
Invent the future with us.
Ampere is a semiconductor design company for a new era, leading the future of computing with an innovative approach to CPU design focused on high-performance, energy efficient AI compute.
As a pioneer in the new frontier of energy efficient high-performance computing, Ampere is part of the Softbank Group of companies driving sustainable computing for AI, Cloud, and edge applications.
Join us at Ampere and work alongside a passionate and growing team - we’d love to have you apply!
About the Role
As an AI Accelerator Principal Software Engineer – Runtime Library, you will lead the design, development, and optimization of AI runtime software that enables multiple state-of-the-art deep learning models to run efficiently on Ampere’s deep learning accelerators. You will work at the intersection of systems software, performance engineering, and AI enablement, helping deliver high-throughput, low-latency inference and a strong foundation for future model and framework support.
What You’ll Achieve:
- Build and evolve an AI Runtime Library for Ampere accelerators that supports execution, scheduling, and lifecycle management of deep learning workloads across multiple model types and popular frameworks.
- Own end-to-end acceleration paths, going deep into the full SW/HW stack—including:
- + Inference serving and integration layers + Compiler/runtime interfaces and graph/IR execution flows + Runtime library architecture (APIs, memory management, operators, execution engines) + Communication mechanisms and device/host orchestration
- Drive HW/SW co-design and optimization to improve:
- + Throughput (tokens/requests per second) + Latency (kernel execution and scheduling efficiency) + Memory efficiency (buffering, paging, reuse, caching) + Overall compute utilization and scaling behavior
- Contribute to AI co-processor/accelerator software enablement, partnering closely with hardware and systems teams to ensure runtime and kernel strategies match accelerator capabilities and constraints.
- Collaborate cross-functionally to integrate runtime components into Ampere platform stacks, ensuring robust deployment on target environments and consistent performance in production-like workloads.
About You:
- BS Computer Science, Computer Engineering, Electrical Engineering, or Software Engineering or related technical field & 8 years of related experience; or MS degree & 6 years; or PhD & 3 years
- Proven experience developing user-mode drivers and/or runtime libraries for GPUs or deep learning accelerators in Linux or RTOS environments.
- Strong expertise in C/C++ and systems-level programming (memory, threading, synchronization, performance profiling).
- Demonstrated background in AI framework enablement, with hands-on experience in one or more of:
- + PyTorch (operator/runtime integration, graph execution, correctness/performance work) + llama.cpp (inference/runtime execution patterns) + ONNX (graph handling, interoperability, execution engines)
- Strong performance engineering skills, including profiling/diagnostics and optimization of execution pipelines, data movement, and compute kernels.
- Ability to operate effectively in a collaborative environment—owning complex components while partnering with compilers, hardware, and platform teams.
What We’ll Offer:
At Ampere we believe in taking care of our employees and providing a competitive total rewards package that includes base pay, cash long-term incentive, and comprehensive benefits. The full base pay range for this role is between $182,000 and $273,000, except in the San Francisco Bay Area where the range is between $195,000 and $292,000.
Our benefits include health, wellness, and financial programs that support employees through every stage of life.
Benefit highlights include:* Premium medical insurance, dental insurance, vision insurance, as well as income protection and a 401K retirement plan, so that you can feel secure in your health and financial future.
- Unlimited Flextime and 10+ paid holidays so that you can embrace a healthy work-life balance.
- A variety of healthy snacks, energizing espresso, and refreshing drinks to keep you fueled and focused throughout the day.
And there is much more than compensation and benefits. At Ampere, we foster an inclusive culture that empowers our employees to do more and grow more. We are excited to share more about our career opportunities with you through the interview process. Our benefits include health, wellness, and financial programs that support employees through every stage of life.
#LI-Hybrid
#LI-Hybrid#LI-DR
#LI-Hybrid
Ampere is an inclusive and equal opportunity employer and welcomes applicants from all backgrounds. All qualified applicants will receive consideration for employment without regard to race, color, national origin, citizenship, religion, age, veteran and/or military status, sex, sexual orientation, gender, gender identity, gender expression, physical or mental disability, or any other basis protected by federal, state or local law.
Salary Context
This $182K-$273K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Ampere Computing, 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 Required
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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($227K) sits 7% above the category median. Disclosed range: $182K to $273K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Ampere Computing AI Hiring
Ampere Computing has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Santa Clara, CA, US. Compensation range: $273K - $273K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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