Senior AI Model Lifecycle Engineer — Fine-Tuning & Scaling

FDE Times; San Francisco (CA); On-site; USD 180,000 - 260,000; Full time

Benefits offered by this job

Health Insurance

Paid Time Off

401(k) Retirement Plan

Job summary

FDE Times is seeking an experienced AI Infrastructure Engineer to build and manage scalable training and fine-tuning systems for foundation models. You will lead distributed orchestration, checkpointing, and cost-efficient scaling while advancing reinforcement learning and tuning pipelines.

Ideal candidates have a strong background in LLMs, generative AI, and distributed training, with hands-on experience in PyTorch, Python, and ML tooling.

Qualifications

• Advanced degree in computer science, engineering, or related field.
• 4+ years of industry experience leading AI projects.
• Experience with generative AI, training and fine-tuning LLMs, and RL techniques.

Responsibilities

• Build and manage scalable fine-tuning and end-to-end training systems for foundation models, including distributed orchestration, checkpointing, failure recovery, and cost-efficient scaling.
• Develop reinforcement learning, reinforcement fine-tuning, distillation, and evaluation pipelines for reproducible training at scale.

Skills

Large Language Models

Generative AI

Multimodal AI

Reinforcement Learning

Reinforcement Fine-Tuning

LLM Fine-Tuning

Supervised Fine-Tuning

Parameter‑Efficient Fine-Tuning

LoRA

PyTorch

Python

Go

Distributed Training

GPU Performance Optimization

Model Evaluation

Experiment Management

Education

Advanced degree in CS/Engineering or related field

Tools

PyTorch

Python

Go

Job description

FDE Times is seeking an experienced AI Infrastructure Engineer to build and manage scalable training and fine-tuning systems for foundation models. You will lead distributed orchestration, checkpointing, and cost-efficient scaling while advancing reinforcement learning and tuning pipelines.

Ideal candidates have a strong background in LLMs, generative AI, and distributed training, with hands-on experience in PyTorch, Python, and ML tooling.