Machine Learning Engineer - Inference Optimization
Remote (world)Full-TimeMiddle
Salary not disclosed
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Job Details
- Required Skills
- PyTorch
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, 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
Responsibilities
- 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
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