Machine Learning Engineer - Inference Optimization - remote job

Featherless AI is hiring a Machine Learning Engineer - Inference Optimization. The role sits in IT and programming and is fully remote. The company places no restriction on where the candidate lives, so you can apply from anywhere.

The advert singles out Machine Learning, so experience with that one tool is what will decide it.

The employer did not publish a figure; that is settled at interview. The advert sets no condition on working hours.

This vacancy passed an automated check: the list excludes any advert requiring a foreign work permit, visa sponsorship, a particular citizenship, or residence in a specific country.

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