Build and train generative 3D models that turn intent into manufacturable geometry across implicit, latent and B-rep targets.
Put physics in the loop with differentiable simulation and physics-driven optimization of generated shapes.
Own the training pipeline end to end and lead the research practice that decides what ships.
MimoLabs creates Mimo, an agentic system that designs geometry, simulates manufacturability, and routes production for physical objects. We are a small, VC-funded technical team based in Detroit and remote across the US.
Own and improve the AI inference serving stack, including prefill/decode disaggregation, continuous batching, KV-cache management, and speculative decoding.
Drive GPU performance engineering by identifying and eliminating bottlenecks using CUDA, Triton, profiling, and kernel optimization.
Set the serving and measurement roadmap, establish engineering standards, and mentor engineers while remaining hands-on with implementation.
Aleroy builds a unified platform for high-performance AI inference, enabling companies to deploy and run AI models at scale through a single API. We work in small, collaborative teams focused on solving complex engineering challenges in scalable AI infrastructure.
Own end-to-end performance optimization for production ML workloads, from bottleneck identification to rollout.
Diagnose ML performance bottlenecks across model execution, frameworks, runtimes, memory, and CPU/GPU utilization.
Design and implement improvements in inference execution, runtime behavior, batching, memory management, and precision.
Reddit is a community of communities, hosting open and authentic conversations across the internet. With over 100,000 active communities and approximately 130 million daily active users, it is one of the web's largest sources of information.
Build and optimize RL infrastructure for large-scale training.
Improve efficiency across compute, memory, networking, and scheduling.
Design low-level optimizations, from kernels to runtime.
Prime Intellect builds the open superintelligence stack, providing full-stack infrastructure for frontier-scale AI post-training and deployment. The company has raised $150M from top investors and is mission-driven, focused on open-source AI and accelerating science.
Build inference systems powering RL research, scaling toward 1–3T parameter models on NVL72 clusters.
Optimize rollout throughput, multi-node MoE inference, and weight-update overhead for long-horizon agents.
Collaborate with model researchers on architecture choices balancing model quality and execution cost.
Prime Intellect is building the open superintelligence stack, unifying compute, environments, and deployment for frontier-scale AI post-training. Backed by $150M in funding, the team is mission-driven and hard-working, focused on accelerating open AI research.
Bring new open-source models into production with a focus on latency and throughput.
Optimize inference systems including batching, KV cache, speculative decoding, and quantization.
Design, deploy, and operate heterogeneous clusters across infrastructure vendors to improve reliability and cost efficiency.
They are an early-stage AI infrastructure startup making large language model inference faster and more cost-efficient. The small team focuses on low-level performance engineering and AI-assisted workflows, with a strong emphasis on technical fundamentals.