AI Engineer: Model Training, Inference & GPU Infra

Agentrys; San Jose, Northern (CA, KY); Hybrid; USD 170,000 - 210,000; Full time

Job summary

Agentrys in San Jose seeks an exceptional AI Engineer to own model training, inference, and infrastructure powering its agentic design workforce. You will drive the full model lifecycle—from data pipelines and pretraining to reinforcement learning and high-performance serving.

You will operate scalable GPU infrastructure, optimize performance, and build evaluation loops that improve models from real execution feedback in production environments.

Qualifications

• > Strong programming in Python and proficiency in a systems language such as C++ or Rust.
• < Deep experience with ML frameworks such as PyTorch or JAX.
• < Hands-on experience with large-scale distributed model training and production inference.

Responsibilities

• > Train, post-train, and fine-tune large language models for agentic engineering workflows, including supervised fine-tuning and RLHF.
• < Build scalable data pipelines for pretraining, post-training, and evaluation across private engineering data.
• < Design and operate distributed training on multi-node GPU clusters with parallelism strategies.
• < Build high-throughput, low-latency inference systems with advanced batching and KV-cache optimization.
• < Write and optimize custom GPU kernels (CUDA, Triton) and profile end-to-end performance.
• < Create core model infrastructure: orchestration, scheduling, checkpointing, observability, cost tracking.
• < Develop automated evaluation systems and reward models to measure agent capability.
• < Integrate models with agent runtimes and production serving stacks.
• < Improve reliability, throughput, and cost efficiency of training and inference.

Skills

Python

C++ or Rust

PyTorch or JAX

Distributed training

GPU optimization

Software engineering

Education

PhD or Master in CS/EE

Tools

Megatron-LM

DeepSpeed

FSDP

Ray

CUDA

Triton

Job description

Agentrys in San Jose seeks an exceptional AI Engineer to own model training, inference, and infrastructure powering its agentic design workforce. You will drive the full model lifecycle—from data pipelines and pretraining to reinforcement learning and high-performance serving.

You will operate scalable GPU infrastructure, optimize performance, and build evaluation loops that improve models from real execution feedback in production environments.