Company Description: Anthology is a community of exceptional engineers and scientists ("Fellows") focused on training the next generation of AI models. Fellows use domain expertise to identify where current models fail, and then create high-quality training environments and data to teach AI how to improve on realistic engineering and research workflows.

We work with leading labs to accelerate progress on the hardest problems in software engineering, biology, medicine, materials science, energy, and beyond. Anthology is part of Polymath, which is backed by Base10, Founders Future, Y Combinator, and other incredible investors.

Role Description: Anthology is looking for experienced GPU kernel and model inference engineers to create and peer-review technical tasks for training and evaluating AI coding agents. These tasks focus on the work that makes models faster and cheaper to run: writing and tuning GPU kernels, and optimizing how models are served.

As an Anthology Fellow, you'll turn problems from your area of expertise into tasks with explicit objectives, working reference solutions,



and verifiers. You'll also review tasks created by other Fellows, checking their technical correctness, difficulty, and quality.

You should have experience in one ore more of the following:
• GPU kernels: CUDA, Triton, CUTLASS, or similar; tiling, memory hierarchy, tensor cores, and operator fusion
• Performance analysis: profiling with Nsight or similar tools, roofline analysis, and finding memory or compute bottlenecks
• Inference systems: vLLM, SGLang, TensorRT-LLM, or similar; batching, KV cache management, paged attention, and scheduling
• Model compression: quantization (INT8, FP8, FP4), sparsity, and their effect on accuracy
• Decoding: speculative decoding, parallel decoding, and other ways to cut latency

In this role, you will:
• Propose original tasks grounded in realistic kernel and inference optimization problems.
• Write specifications that define the target workload, har

📌 ML Engineer: Inference Optimization and GPU Kernels (India)
🏢 Anthology
📍 India