Applied AI/ML Engineer

Boundless Networks, Inc.

1d•Remote

About The Position

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.

Requirements

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 and a clear desire to go deep here
Strong Python and PyTorch
Working understanding of GPU execution: batching, memory, and basic CUDA concepts
Comfort operating in ambiguity with a strong bias for action
Candidates must include a public GitHub profile in their application.
The GitHub profile should demonstrate a minimum of 1 year of activity/history.
Applications that do not include a GitHub profile, or show insufficient activity, will not be considered.

Nice To Haves

Direct experience with slime, prime-rl, the verifiers library, or Megatron-LM
Distributed training experience (FSDP, TP/PP/DP parallelism)
Quantization (FP8/INT8), P/D disaggregation, or speculative decoding
Experience with verifiable inference or large-scale distributed systems
Kubernetes and container-based deployment
Familiarity with GPU fleet orchestration (Ray, SkyPilot, Slurm)

Responsibilities

Own AI features and products from prototype through production — model selection, serving, evaluation, and iteration — shipping working software rather than research artifacts.
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.
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.
Build eval harnesses and benchmarks that measure quality, throughput, and cost together, and use them to drive fast, data-informed iteration.
Partner with Infrastructure on GPU scheduling and fleet utilization, and with Product on what to build next and why.