Applied AI/ML Engineer

📋 Job Overview

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.

🏢 About Boundless Networks, Inc.

Boundless Networks is a company focused on coordinating GPU compute at scale and building toward becoming a leader in AI.

🎯 The Role

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.

✅ Key Responsibilities

• 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.

📌 Required 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 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

⭐ Desirable Experience

• 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)

🎁 Benefits

Competitive salary (proposed band b/t US$175k and $250k annually) + equity allocation
Health, dental, vision (for U.S. employees; region-adjusted globally)
Flexible PTO
Professional development and conference travel budget
Remote-first with regular off-sites and a high-trust, high-velocity team environment

🛂 Visa & Eligibility

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.