Applied AI/ML Engineer - Remote, Equity, Flexible PTO

Boundless Networks, Inc.; United States; Remote; USD 150,000 - 210,000; Full time

Benefits offered by this job

Salary + equity allocation

Health insurance

Flexible PTO

Professional development budget

Remote-first with off-sites

Job summary

Boundless Networks, Inc. is seeking an Applied AI/ML Engineer to ship AI-powered products end-to-end on our growing GPU inference fleet. You will own everything from low-latency serving to post-training pipelines, iterating quickly on customer and internal feedback.

You will deploy and optimize LLM inference using vLLM/SGLang/TensorRT-LLM, build RL/post-training harnesses, and drive data-informed iteration across the stack with product and infrastructure partners.

Qualifications

• 3+ years shipping ML/AI systems to production.
• Hands-on experience serving LLM inference with vLLM, SGLang, or TensorRT-LLM.
• Experience with RL / post-training methods (GRPO, PPO, DPO, or SFT), or strong adjacent experience.
• Strong Python and PyTorch experience.
• Working understanding of GPU execution: batching, memory, CUDA concepts.
• Comfort operating in ambiguity with a strong bias for action.

Responsibilities

• Own AI features end-to-end from prototype through production.
• Deploy and optimize LLM inference across the GPU fleet.
• Build and operate RL and post-training harnesses.
• Develop eval harnesses and benchmarks measuring quality, throughput, and cost.
• Collaborate with Infrastructure on GPU scheduling and with Product on roadmap decisions.

Skills

ML/AI systems

LLM inference

Python

PyTorch

GPU concepts

Ambiguity tolerance

Tools

vLLM

SGLang

TensorRT-LLM

Megatron-LM

Job description

Boundless Networks, Inc. is seeking an Applied AI/ML Engineer to ship AI-powered products end-to-end on our growing GPU inference fleet. You will own everything from low-latency serving to post-training pipelines, iterating quickly on customer and internal feedback.

You will deploy and optimize LLM inference using vLLM/SGLang/TensorRT-LLM, build RL/post-training harnesses, and drive data-informed iteration across the stack with product and infrastructure partners.