Machine Learning Engineer — Inference Optimization
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Before you apply
LocationRemote (world)
Work moderemote
Typefull-time
SenioritySenior
DepartmentResearch
EquityIncluded
Company size51–200 people
PostedJan 22, 2026 · 9mo ago
Verified live3d ago
May no longer be actively hiring
Posted by the company on Jan 22, 202671% of this company's open roles have been listed for 90+ days
At a glanceSummarised by Seekless from the posting.
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
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
Skills
PyTorch
CUDA
ROCm
Triton
TensorRT
ONNX Runtime
vLLM
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
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Optimize inference latency, throughput, and cost for large-scale ML models in production
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Profile and bottleneck GPU/CPU inference pipelines (memory, kernels, batching, IO)
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Implement and tune techniques such as:
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Quantization (fp16, bf16, int8, fp8)
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KV-cache optimization & reuse
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Speculative decoding, batching, and streaming
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Model pruning or architectural simplifications for inference
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Collaborate with research engineers to productionize new model architectures
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Build and maintain inference-serving systems (e.g. Triton, custom runtimes, or bespoke stacks)
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Benchmark performance across hardware (NVIDIA / AMD GPUs, CPUs) and cloud setups
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Improve system reliability, observability, and cost efficiency under real workloads
What We’re Looking For
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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
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Real ownership over performance-critical systems
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Direct impact on product reliability and unit economics
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Close collaboration with research, infra, and product
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Competitive compensation + meaningful equity at Series A
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A team that cares about engineering quality, not hype