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Boundless Networks, Inc.
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Boundless is coordinating GPU compute at scale and building toward becoming a leader in AI. As an Applied AI/ML Engineer, you'll ship AI-powered products end-to-end on top of our growing GPU inference fleet — owning everything from serving low-latency inference to standing up reinforcement-learning post-training pipelines. This is a builder's role: you take an idea from prototype to production, tune it for throughput and cost on real GPUs, and iterate fast on customer and internal feedback.
You should be comfortable operating with a high degree of autonomy, navigating ambiguity, and defaulting to a strong bias for action.
End-to-End AI Product Delivery: Own AI features and products from prototype through production — model selection, serving, evaluation, and iteration — shipping working software rather than research artifacts.
Inference Serving: Deploy and optimize LLM inference across the fleet using vLLM and SGLang. Tune continuous batching, KV-cache management, quantization, speculative decoding, and multi-model routing to maximize throughput and minimize latency and cost per token.
RL & Post-Training Harnesses: Build and operate reinforcement-learning and post-training pipelines using slime (Megatron-LM + SGLang) and Prime Intellect (prime-rl + the Environments Hub / verifiers). This includes reward and verifier design, rollout orchestration, weight synchronization, and keeping long-running training stable.
Evaluation & Iteration: Build eval harnesses and benchmarks that measure quality, throughput, and cost together, and use them to drive fast, data-informed iteration.
Work Across the Stack: Partner with Infrastructure on GPU scheduling and fleet utilization, and with Product on what to build next and why.
At Boundless, we take care of our people, because building the future of AI compute starts with an empowered team. Here's what you can expect when you join us:
We are a global team, and applicants from around the world are welcome to apply.
Originally posted on Himalayas
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