Machine Learning Engineer, Agentic AI

Qualcomm

Confirmed live today High trust

Quick summary

Work type
On-site
Location
San Diego, CA
Salary
$114,500–$171,700 / yr
Posted
34 days ago
Freshness
Confirmed live today
Closes
Mar 22, 2027

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $218k
This role $143k
$96k $283k
below market most similar roles pay here above market

This role pays less than 94% of similar roles. Most pay $187,437–$249,000 — the blue band above. At the midpoint, this role pays about $143k versus about $218k for comparable roles.

Based on 240 similar postings.

Employer

About Qualcomm

Qualcomm is a leading American semiconductor and telecommunications company based in San Diego, CA.

Qualcomm currently has 575 open roles on FindRole.

Listed pay typically runs $147,900–$221,900 across 550 roles with salary data.

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At a glance

TL;DR · Machine Learning Engineer, Agentic AI

The Machine Learning Engineer – Agentic AI will join the Core Enterprise AI team within the Modem Technologies Software group. This role involves designing, developing, and deploying next-generation AI systems powered by Large Language Models, Agentic AI, and Hybrid AI architectures. You will build enterprise-grade solutions capable of reasoning, planning, and autonomous task execution while optimizing RAG and knowledge-grounded architectures. Key responsibilities include developing self-improving agents using reinforcement learning, supervised fine-tuning, and distillation, as well as creating evaluation and safety frameworks. Required skills include Python, PyTorch, and TensorFlow, alongside experience with Langchain, LlamaIndex, and Transformers. You will utilize vector databases, embedding models, and cloud platforms like AWS, GCP, and Azure to solve complex enterprise problems through multi-agent orchestration and intelligent task automation.

What does a Machine Learning Engineer earn in California?

Median $235750 from 200 postings across 34 companies.

See salary data

What you'll do

  • Design and implement Agentic AI systems capable of autonomous reasoning, planning, and multi-step execution.
  • Build and optimize RAG, Hybrid AI, and knowledge-grounded architectures using enterprise data and external tools.
  • Develop self-improving AI agents using feedback loops, reinforcement learning, and continuous learning frameworks.
  • Apply LLM post-training techniques including supervised fine-tuning, preference optimization, and distillation.
  • Create evaluation, benchmarking, observability, and safety frameworks to measure agent reliability and business impact.
  • Build and maintain multi-agent systems and orchestration frameworks for collaborative workflows and task automation.

What we're looking for

  • Bachelor's degree in Computer Engineering, Computer Science, Electrical Engineering, or a related field.
  • Strong programming skills in Python and experience with ML frameworks like PyTorch or TensorFlow.
  • Practical experience with LLM deployments, fine-tuning, and RAG architectures.
  • Experience with vector databases, embedding models, and modern AI/ML infrastructure or cloud platforms (AWS, GCP, Azure).
  • Experience with containerization, microservices architecture, and ML ops deployment pipelines (preferred).
  • Knowledge of prompt engineering, chain-of-thought techniques, and Reinforcement Learning (preferred).
  • Experience with popular LLM frameworks such as Langchain, LlamaIndex, or Transformers (preferred).
  • Contributions to open-source AI projects (preferred).

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