Featherless AI
Machine Learning Engineer — Inference Optimization
Vaga remota de AI Optimization Engineer com fit claro de localização do candidato.
Publicada25 de jul. de 2026
Países elegíveis5 países aceitos
Sinal de senioridadeMiddle
Modelo de trabalhoRemoto
Locais aceitos para candidatos
CanadáAlemanhaÍndiaReino UnidoEstados Unidos
Resumo da vaga
Machine Learning Engineer — Inference Optimization
Requisitos e responsabilidades
Conteúdo da vaga extraído em seções para revisão mais rápida.
What You’ll Do
- 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 & reuseSpeculative decoding, batching, and streamingModel pruning or architectural simplifications for inference
- 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
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
What We’re Looking For
- 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
Nice to haves
- 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
Why Join Us
- 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
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