Sr. Software Engineer - AI Triton Communication

San Jose, CA, US Senior AI Software Engineer

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Skills & Technologies

Pytorch

About This Role

AI job market dashboard showing open roles by category

Overview:

ADVANCE YOUR CAREER. ADVANCE THE WORLD.

At AMD, we believe technology can change lives for the better. It can heal us, entertain us, and make us more connected, productive, and understanding of the world around us. And we’re looking for talent who feel the same: people who want to leave the planet better than they found it, those who don’t shy away from humanity’s challenges but are determined to help solve them.

AMD is powering the next generation of supercomputing, high\-performance computing, cloud, and AI. Whether you’re designing next\-gen processors, enabling AI breakthroughs, or creating go\-to\-market plans, every role at AMD contributes to something bigger — technology that moves the world forward.

Responsibilities:

THE ROLE:

Triton is a widely adopted language and compiler for high\-performance GPU kernels, powering major AI frameworks such as PyTorch, vLLM, and SGLang. As AI workloads increasingly scale across multiple GPUs and nodes, first\-class support for distributed execution and communication in Triton is strategically critical to enabling efficient large\-scale training and inference on AMD Instinct Accelerators.

AMD GPUs are an official Triton backend, and delivering industry\-leading distributed performance and scalability on AMD Instinct accelerators is a key priority. The performance, scalability, and usability of Triton directly impact the competitiveness of AMD hardware in large\-scale AI deployments.

In this role, you will advance the Triton compiler and runtime stack for AMD CDNA and next\-generation GPU architectures by building native distributed execution and communication capabilities. You will develop compiler and runtime infrastructure that enables efficient inter\-GPU communication, scalable execution, and optimal hardware utilization. You will work across compiler, runtime, and hardware layers, collaborating closely with GPU architecture and software teams to help establish AMD GPUs as a best\-in\-class platform for Triton\-based distributed AI. THE PERSON:

The ideal candidate has deep expertise in GPU architecture, compiler technologies, and distributed GPU systems, with proven experience optimizing workloads at multi\-GPU scale. You are comfortable working across the full execution stack — from compiler and runtime to hardware — and understand how GPU execution, memory hierarchy, and inter\-GPU communication impact performance.

You have experience working close to the GPU runtime, communication stack, or compiler backend, and are motivated to build native distributed execution and communication capabilities tightly integrated with the compiler and runtime to maximize scalability and hardware utilization. You thrive on solving complex system\-level performance challenges and delivering scalable, high\-performance GPU infrastructure. KEY RESPONSIBILITIES:* Design and develop native distributed communication and execution capabilities within the Triton AMDGPU backend, enabling scalable multi\-GPU execution for large\-scale AI workloads

  • Design and implement Triton compiler and runtime mechanisms for native GPU\-initiated communication, including collective operations, remote memory access, synchronization, and distributed execution primitives
  • Drive performance optimization across compute and communication, including inter\-GPU data movement, communication/computation overlap, memory hierarchy utilization, and GPU\-driven scheduling efficiency
  • Develop and optimize distributed Triton kernels and execution models to achieve high performance, scalability, and efficient hardware utilization for AI workloads
  • Analyze, profile and debug complex cross\-stack issues spanning Triton compiler, runtime, ROCm stack, and GPU hardware execution
  • Collaborate closely with GPU architecture, compiler, runtime, and performance teams to co\-design and enable next\-generation distributed GPU programming and execution capabilities
  • Contribute to open\-source Triton and ROCm distributed ecosystem, driving innovation in distributed GPU computing

PREFERRED EXPERIENCE:* Deep experience in compiler development, GPU software, distributed systems, or performance engineering

  • Familiarity or hands\-on experience with Triton compiler and runtime
  • Deep understanding of modern GPU architectures, including execution model, memory hierarchy (LDS, L2, HBM), scheduling, occupancy, and hardware performance characteristics
  • Good understanding of GPU runtime systems, communication stacks, and multi\-GPU interconnects such as XGMI, NVLink, PCIe, or InfiniBand and their performance implications
  • Familiarity with distributed GPU communication libraries such as RCCL, NCCL, NVSHMEM, rocSHMEM, or MPI and similar technologies
  • Experience developing, optimizing, and scaling workloads across multiple GPUs, including inter\-GPU communication, synchronization, and communication/computation overlap
  • Strong experience with GPU programming using Triton, HIP, CUDA, or similar parallel programming environments
  • Strong knowledge of MLIR and/or LLVM internals
  • Experience profiling, debugging, and optimizing performance across compiler, runtime, and hardware layers
  • Familiarity with ROCm, HIP, CUDA, or similar GPU programming ecosystems, including performance profiling and optimization tools
  • Experience optimizing large\-scale AI, machine learning or HPC workloads across multi\-GPU systems
  • Experience contributing to open\-source projects and working in collaborative, cross\-functional engineering environments
  • Strong problem\-solving, communication, and technical leadership skills

PREFERRED ACADEMIC CREDENTIALS:* Bachelor’s or Master's Degree in Computer Engineering, Computer Science, Electrical Engineering or equivalent practical experience

This role is not eligible for visa sponsorship.

\#LI\-G11

\#LI\-HYBRID

Qualifications:

*Benefits offered are described:* AMD benefits at a glance. *AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee\-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third\-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process.* *AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available* *here.* *This posting is for an existing vacancy.*

Role Details

Company AMD
Title Sr. Software Engineer - AI Triton Communication
Location San Jose, CA, US
Category AI Software Engineer
Experience Senior
Salary Not disclosed
Remote No

About This Role

AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.

The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.

Across the 4,317 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At AMD, this role fits into their broader AI and engineering organization.

AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.

What the Work Looks Like

A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.

AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.

Skills Required

Pytorch (15% of roles)

Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.

Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.

Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.

Compensation Benchmarks

AI Software Engineer roles pay a median of $218,500 based on 729 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.

Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.

AMD AI Hiring

AMD has 21 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Product Manager, AI Software Engineer. Positions span San Diego, CA, US, Austin, TX, US, San Jose, CA, US.

Location Context

Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).

Career Path

Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.

From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.

If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.

What to Expect in Interviews

Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.

When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.

AI Hiring Overview

The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.

The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).

AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.

The AI Job Market Today

The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.

Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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

Based on 729 roles with disclosed compensation, the median salary for AI Software Engineer positions is $218,500. Actual compensation varies by seniority, location, and company stage.
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
About 15% of the 4,317 AI roles we track offer remote work. Remote availability varies by company and seniority level, with senior and leadership roles more likely to offer location flexibility.
AMD is among the companies actively hiring for AI and ML talent. Check our company profiles for detailed breakdowns of open roles, salary ranges, and hiring trends.
Common next steps from AI Software Engineer positions include Staff Engineer, AI Architect, Engineering Manager. Progression depends on whether you lean toward technical depth, people management, or product strategy.

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