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Applied AI/ML Engineer

14 hours, 35 minutes ago
United States
Full-time
Mid Level
Machine Learning Engineer
Software Development
Docker Kubernetes Python PyTorch Reinforcement Learning
Apply Now
Boundless

Boundless

Boundless provides a protocol for accessing verifiable compute across various blockchain networks, enabling rapid upgrades to zero-knowledge rollups while ensuring transparency and security in trade settlements.

Internet Software & Services
Founded 2005
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Description

  • Own AI features and products from prototype through production, including model selection, serving, evaluation, and iteration.
  • Deploy and optimize LLM inference across the GPU fleet using vLLM and SGLang.
  • Tune serving systems for throughput, latency, and cost using techniques such as continuous batching, KV-cache management, quantization, speculative decoding, and multi-model routing.
  • Build and operate reinforcement-learning and post-training pipelines using slime and Prime Intellect tooling.
  • Design reward functions and verifiers, orchestrate rollouts, synchronize weights, and keep long-running training jobs stable.
  • Build evaluation harnesses and benchmarks that measure quality, throughput, and cost together.
  • Use evaluation results and customer/internal feedback to drive fast iteration on products and infrastructure.
  • Partner with Infrastructure on GPU scheduling and fleet utilization.
  • Collaborate with Product on what to build next and why.

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 such as GRPO, PPO, DPO, or SFT, or strong adjacent experience with a clear desire to go deep.
  • Strong Python and PyTorch skills.
  • Working understanding of GPU execution, including batching, memory, and basic CUDA concepts.
  • Comfort operating in ambiguity with a strong bias for action.
  • Public GitHub profile required in the application.
  • At least 1 year of GitHub activity/history is required.
  • Direct experience with slime, prime-rl, the verifiers library, or Megatron-LM is preferred.
  • Distributed training experience with FSDP or TP/PP/DP parallelism is preferred.
  • Experience with quantization (FP8/INT8), P/D disaggregation, or speculative decoding is preferred.
  • Experience with verifiable inference or large-scale distributed systems is preferred.
  • Kubernetes and container-based deployment experience is preferred.
  • Familiarity with GPU fleet orchestration tools such as Ray, SkyPilot, or Slurm is preferred.

Benefits

  • Competitive salary of US$175k-$250k annually plus equity allocation.
  • Health, dental, and vision coverage for U.S. employees, with region-adjusted coverage globally.
  • Flexible PTO.
  • Professional development and conference travel budget.
  • Remote-first work with regular off-sites.
  • High-trust, high-velocity team environment.
  • Global hiring, with applicants from around the world welcome.

Interested in this position?

Apply directly on the company website

Apply Now

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