Featherless
via Ashby
Machine Learning Engineer — Inference Optimization
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 1d ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 236d ago
The date the source published, not the day we noticed it (2026-01-22). Last seen at its source just now.
Is it remote?
Remote (world)
That is the location the employer filed this posting under. Quoted as written — we do not re-word the source's own location.
Who may apply?
Available worldwide
The description states no restriction of its own. This is the source's own tag.
Pay not stated
Similar roles pay $110.6k–163.4k/yr
Middle 50% of 26 listings that do state pay — Engineering · all levels · Worldwide · USD/year. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
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ashby employer's own board first seen 39d ago · last seen just now
The listing
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