Software Development Engineer 5

Position Overview

• Job Title: Software Development Engineer 5 – Agentic ML Infrastructure
• Job Class: Engineering
• Level: SDE 5
• Engagement: 6-month contract
• Location: United States — remote/hybrid per AMD policy
• Reports To: Technical Lead, AMD AGI

About the Role

AMD is building agentic software to improve how teams develop, deploy, and operate machine learning workloads on AMD GPUs.

This senior contract role focuses on designing and deliveringproduction-grade intelligent agents across the AMD software stack, including:

• Training and inference frameworks
• Cluster tooling
• Performance analysis
• Operational workflows for large-scale GPU deployments

The role will lead implementation ofPython systems that combine LLM orchestration, tool integration, and rigorous evaluation to automate high-value engineering work, including:

• Investigating distributed job failures
• Interpreting telemetry and profilers
• Accelerating performance analysis
• Reducing manual toil in ML operations

This is asenior individual contributor engagement. The engineer is expected to own significant components end-to-end, make sound technical decisions within program direction, and deliver maintainable code with minimal supervision.

Key Responsibilities

Agent Framework & LLM Engineering

• Architect and implement Python agent frameworks, including:
  • Tool adapters
  • Orchestration loops
  • Structured outputs
  • Session handling
  • CLI or service interfaces suitable for production use
• Build LLM-powered workflows usingLiteLLM, DSPy, or equivalent.
• Implement bounded prompts, allowlisted tools, budget caps, structured JSON outputs, and evidence-backed reporting.

ML & GPU Infrastructure

• Integrate agents with the AMD GPU and ML software ecosystem, including:
  • ROCm tooling
  • PyTorch
  • Distributed training and inference stacks
  • Profilers
  • Cluster metrics
  • Log pipelines
  • Debug utilities
• Solve ML operations problems on training and inference clusters, including:
  • Multi-node failure analysis
  • Performance regression investigation
  • Configuration and launch issues
  • Operational automation

Quality, Testing & Evaluation

• Own agent quality and evaluation infrastructure.
• Develop fixture-driven tests and regression suites.
• Implement JSON Schema validation and accuracy metrics.
• Integrate testing and evaluation into CI before rollout.

Deployment & Documentation

• Deliver deployment-ready artifacts, including:
  • Docker packaging
  • Kubernetes packaging
  • Runbooks
  • Technical documentation
• Apply security-conscious defaults for customer-controlled environments.

Collaboration & Delivery

• Partner with framework, performance, and field teams.
• Incorporate code review feedback and iterate based on production and pilot learnings.
• Own significant technical components independently and deliver production-quality solutions with minimal supervision.

Required Qualifications

Education & Experience

• Bachelor's degree or higher in:
  • Computer Science
  • Computer Engineering
  • Electrical Engineering
  • Related field
• Equivalent practical experience accepted.
• 10+ years of professional software engineering experience.
• 5+ years of experience developing production Python systems at scale.

Distributed ML & GPU Systems

• Deep experience with distributed ML training or inference on GPU clusters.
• Experience with:
  • Multi-node jobs
  • Collective communication failures
  • Log and metric correlation across ranks and nodes
• Strong hands-onPyTorch experience.
• Production familiarity with large-scale ML stacks such as:
  • Megatron-LM
  • DeepSpeed
  • TorchTitan
  • vLLM
  • Equivalent technologies

ML Debugging & Performance Engineering

• Proven ML debugging and performance engineering experience across multiple failure modes, including:
  • Throughput regression
  • Memory errors
  • Numerical instability
  • Misconfiguration
  • Profiler or trace analysis

LLM Agents & Software Engineering

• Demonstrated experience delivering LLM agent or orchestration systems in production or near-production environments.
• Experience with:
  • Tool routing
  • Structured outputs
  • Reliability
  • Testability
• Track record of delivering complex software on schedule.
• Strong capabilities in:
  • Clean, maintainable code
  • Automated testing
  • Code review
  • Technical communication
• Ability to work independently, prioritize ambiguous requirements, and align weekly with a technical lead.

Preferred Qualifications

• Open-source ML development, including:
  • Upstream framework repositories
  • Community CI patterns
  • OSS tooling integration
• ML performance optimization, including:
  • Parallelism
  • Throughput/MFU tuning
  • Roofline analysis
  • Profiler-driven investigation
• ROCm and GPU cluster operations, including:
  • Metrics
  • Profilers
  • Health monitoring
  • Slurm
  • Kubernetes job environments
• Security-aware agent deployment, including:
  • Secret handling
  • Egress control
  • Customer VPC constraints
  • On-premises deployment constraints

Working Model

• Duration: 12 months
• Schedule: Full-time contract
• Cadence: Weekly sync with technical lead
• Delivery Model: Milestone-driven
• Scope: Implementation across AMD agent and ML infrastructure programs
• Priorities: Allocation may shift based on program priorities at the direction of the technical lead

What This Role Is Not

This position isnot focused on:

• Graphics driver development
• Kernel development
• Low-level GPU driver development
• Research-only work without production deliverables
• Junior or mid-level implementation

SDE 5 expectations: Senior ownership, strong technical depth, and demonstrated expertise acrossML systems and agent engineering.