Staff AI Machine Learning Engineer

A10 Networks, Inc; San Francisco (CA); On-site; USD 110,000 - 145,000; Full time

Job summary

A leading tech firm in San Francisco is seeking a Deep Learning Engineer to enhance the reliability and performance of AI models. The role focuses on developing safety mechanisms in Large Language Models and collaborating with cross-functional teams to drive advancements in AI safety. Candidates should have a degree in Computer Science and expertise in Python and deep learning frameworks like TensorFlow or PyTorch. Targeted compensation is $110,000 - $145,000, depending on experience and skills.

Qualifications

• Experience in developing and deploying Large Language Models (LLMs) focusing on architectures like GPT and BERT.
• Strong programming skills in Python with deep learning frameworks.
• Experience with distributed training and cloud computing platforms.

Responsibilities

• Design and implement safety-focused frameworks for LLMs.
• Develop risk mitigation techniques for reliable AI behavior.
• Collaborate to integrate safety mechanisms in AI workflows.
• Optimize LLM architectures for performance and scalability.

Skills

Deep Learning

Python programming

Communication

Problem-solving

Education

Bachelor's or Master's degree in Computer Science or related field

Tools

TensorFlow

PyTorch

AWS

Azure

Google Cloud

Job description

Position Overview

We are looking for a talented and experienced Deep Learning Engineer specializing in Large Language Models (LLMs) to join our dynamic team. In this role, you will play a pivotal part in enhancing the reliability, safety, and performance of AI models and systems. You will work closely with AI researchers and product teams to drive cutting-edge advancements in AI safety and responsible AI solutions.

Responsibilities

• Assist in designing and implementing end-to-end safety-focused frameworks for LLMs
• Develop and apply risk mitigation techniques, including safe inference strategies to ensure reliable AI behavior
• Identify vulnerabilities in AI systems and contribute to adversarial testing, bias detection, and mitigation strategies
• Collaborate with cross-functional teams to integrate safety mechanisms into AI workflows and pipelines
• Design and optimize LLM architectures to improve performance, scalability, and efficiency
• Fine‑tune pre‑trained LLMs on domain‑specific datasets to improve task performance
• Stay up to date with the latest research papers, techniques, and advancements in deep learning and related fields
• Strong software engineering and programming skills, and ability to quickly develop working prototypes from research ideas

Requirements

• Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related field
• Proven experience in developing and deploying Large Language Models (LLMs), with a focus on architectures such as GPT, BERT, and their variants
• Strong programming skills in Python and experience with deep learning frameworks such as TensorFlow or PyTorch
• Knowledge of distributed training techniques and experience with cloud computing platforms (e.g., AWS, Azure, Google Cloud)
• Excellent problem‑solving skills and ability to work independently and in a team environment
• Strong communication and collaboration skills

Targeted compensation guideline: $110,000 - $145,000. Compensation will vary based on number of factors, including market demand for specific skills, role type, job level, and individual qualifications. Final salary offers are determined by considerations including, but not limited to, subject matter expertise, demonstrated skill level, relevant experience, geographic location, education, certifications, and training.