Machine Learning Engineer — Inference Optimization
At a glance
Mid-level Machine Learning Engineering role at Featherless AI. Remote · full-time.
Pay not stated Machine Learning Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Reduce inference delay, increase throughput, and control serving costs for production ML models
- Trace GPU and CPU pipelines to find bottlenecks in memory, kernels, batching, and data movement
- Tune quantization, KV-cache reuse, speculative decoding, batching, and model simplifications
- Bring new architectures from research into production and maintain the systems that serve them
- Compare performance across NVIDIA and AMD GPUs, CPUs, and cloud environments while improving reliability
What you bring
- You have substantial experience improving ML inference or building high-performance machine learning systems
- You understand deep learning internals such as attention, memory layout, and compute graphs
- You have deployed models with PyTorch or a comparable framework and tuned GPU performance
- You have scaled inference for real users, beyond research benchmarks
Who this fits
This mid-level, remote role suits you if you want hands-on ownership of performance-critical inference systems. You’ll work across research and production, and should be comfortable taking ownership amid changing priorities.
From the employer
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