Remote AI/ML Engineer — Production Inference & RL

Boundless; Northern (KY); Hybrid; USD 175,000 - 250,000; Full time

Benefits offered by this job

Competitive salary + equity

Health/dental/vision

Flexible PTO

Dev & conference budget

Remote-first with off-sites

Job summary

Boundless is building toward AI leadership by delivering end-to-end AI features on a growing GPU inference fleet. You will own AI products from prototype to production, optimizing LLM inference, and implementing RL/post-training harnesses to ensure stability and efficiency.

You’ll work with vLLM, SGLang, and Megatron-based tools, focusing on throughput, latency, and cost per token while operating with autonomy in a fast-moving environment.

Qualifications

• 3+ years shipping ML/AI systems to production.
• Experience with LLM inference using vLLM, SGLang, or TensorRT-LLM.
• Experience with RL/post-training methods (GRPO, PPO, DPO, or SFT).
• Strong Python and PyTorch development skills.
• Understanding of GPU execution: batching, memory, CUDA basics.
• Ability to operate with autonomy and bias for action.

Responsibilities

• Own AI features from prototype to production — model selection, serving, evaluation, and iteration.
• Deploy and optimize LLM inference across GPU fleet.
• Build RL and post-training harnesses using slime (Megatron-LM + SGLang) and Prime Intellect (prime-rl + the Environments Hub / verifiers).
• Build eval harnesses and benchmarks measuring quality, throughput, and cost.
• Collaborate with Infrastructure on GPU scheduling and fleet utilization, and with Product on what to build next.

Skills

ML systems production

LLM inference experience

Python

PyTorch

CUDA basics

Ambiguity tolerance

LLM inference stacks

RL / post-training methods

Distributed training

Tools

Kubernetes

Container deployment

Ray

Slurm

Job description

Boundless is building toward AI leadership by delivering end-to-end AI features on a growing GPU inference fleet. You will own AI products from prototype to production, optimizing LLM inference, and implementing RL/post-training harnesses to ensure stability and efficiency.

You’ll work with vLLM, SGLang, and Megatron-based tools, focusing on throughput, latency, and cost per token while operating with autonomy in a fast-moving environment.