Machine Learning Engineer — Inference Optimization

We’re looking for a Machine Learning Engineer to own and push the limits of model inference performance at scale. You’ll work at the intersection of research and production—turning cutting-edge models into fast, reliable, and cost-efficient systems that serve real users.

This role is ideal for someone who enjoys deep technical work, profiling systems down to the kernel/GPU level, and translating research ideas into production-grade performance gains.

• Optimize inference latency, throughput, and cost for large-scale ML models in production

• Profile and bottleneck GPU/CPU inference pipelines (memory, kernels, batching, IO)

• Implement and tune techniques such as:
  • Quantization (fp16, bf16, int8, fp8)
  • KV-cache optimization & reuse

  • Speculative decoding, batching, and streaming

  • Model pruning or architectural simplifications for inference

• Collaborate with research engineers to productionize new model architectures

• Build and maintain inference-serving systems (e.g. Triton, custom runtimes, or bespoke stacks)

• Benchmark performance across hardware (NVIDIA / AMD GPUs, CPUs) and cloud setups

• Improve system reliability, observability, and cost efficiency under real workloads

• 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, or kernel-level optimizations)

• Experience scaling inference for real users (not just research benchmarks)

• Comfortable working in fast-moving startup environments with ownership and ambiguity

• Experience with LLM or long-context model inference

• Knowledge of inference frameworks (TensorRT, ONNX Runtime, vLLM, Triton)

• Experience optimizing across different hardware vendors

• Open-source contributions in ML systems or inference tooling

• Background in distributed systems or low-latency services

• 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

Originally posted on Himalayas