Applied AI/ML Engineer

Summary

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.

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

Skills

• 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
• 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)

Qualifications

Must Haves

• 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)

Benefits

• 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
• Competitive salary + equity allocation