Machine Learning Engineer - Inference Optimization - vaga remota

A empresa Featherless AI está com uma vaga aberta de Machine Learning Engineer - Inference Optimization. A posição fica na área de TI e programação e é totalmente remota. A empresa não impõe restrição sobre onde o candidato mora, então você pode se candidatar de onde estiver.

O anúncio destaca Machine Learning, então a experiência com essa ferramenta é o que vai pesar.

A empresa não publicou nenhum valor, isso é acertado na entrevista. O anúncio não impõe condição nenhuma de horário.

Esta vaga passou por uma verificação automática: anúncios que exigem autorização de trabalho estrangeira, patrocínio de visto, uma nacionalidade específica ou residência em um país indicado não entram na lista.

About the Role 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. 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 & 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

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 Have

* 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

Originally posted on Himalayas