Machine Learning Engineer — Inference Optimization

📋 Description

• Optimize inference latency, throughput, and cost for large-scale ML models in production
• Profile GPU/CPU inference pipelines (memory, kernels, batching, IO)
• Quantization (fp16, bf16, int8, fp8)
• KV-cache optimization & reuse
• Speculative decoding, batching, and streaming
• Model pruning or architectural simplifications for inference

🎯 Requirements

• Strong experience in ML inference optimization or high-performance ML systems
• Solid understanding of deep learning internals (attention, memory layout, compute graphs)
• Hands-on experience with PyTorch (or similar) and model deployment
• Familiarity with GPU performance tuning (CUDA, ROCm, Triton)
• Experience scaling inference for real users
• Comfortable working in fast-moving startup environments with ownership and ambiguity

🎁 Benefits

• Real ownership over performance-critical systems
• Direct impact on product reliability and unit economics
• Close collaboration with research, infra, and product
• Competitive compensation + meaningful equity at Series A
• A team that cares about engineering quality, not hype