✓ Screened eligible
Why you can actually get this job
The posting explicitly states 'Remote (world)' with no geographic, work authorization, timezone, language, or residency restrictions. The role is a full-time employee position with no visa sponsorship clause. The target user meets all technical requirements (Python, ML frameworks, inference optimization experience).
“Remote (world)”
Geographic scope — Explicitly states worldwide remote scope with no geographic restrictions.
“Remote (world)”
Physical presence — No physical presence, relocation, or office-based requirements mentioned anywhere in the posting.
“Remote (world)”
Timezone — No timezone restriction or overlap window specified; worldwide remote scope implies flexibility.
Role Overview
We are seeking an AI Researcher with deep experience in inference optimization to design, evaluate, and deploy high-performance inference systems for large-scale machine learning models. You will work at the intersection of model architecture, systems engineering, and hardware-aware optimization , improving latency, throughput, and cost efficiency across real-world production environments.
Key Responsibilities
- Research and develop techniques to optimize inference performance for large neural networks.
- Improve latency, throughput, memory efficiency, and cost per inference .
- Design and evaluate model-level optimizations (quantization, pruning, KV-cache optimization, architecture-aware simplifications).
- Implement systems-level optimizations (dynamic batching, kernel fusion, multi-GPU inference, prefill vs decode optimization).
- Benchmark inference workloads across hardware accelerators.
- Collaborate with engineering teams to deploy optimized inference pipelines .
- Translate research insights into production-ready improvements .
Required Qualifications
- Strong background in machine learning, deep learning, or AI systems .
- Hands-on experience optimizing inference for large-scale models .
- Proficiency in Python and modern ML frameworks (e.g., PyTorch).
- Experience with inference tooling (e.g., Triton, TensorRT, vLLM, ONNX Runtime).
- Ability to design experiments and communicate results clearly.
Preferred / Nice-to-Have Qualifications
- Experience deploying production inference systems at scale .
- Familiarity with distributed and multi-GPU inference .
- Experience contributing to open-source ML or inference frameworks .
- Authorship or co-authorship of peer-reviewed research papers in machine learning, systems, or related fields.
- Experience working close to hardware (CUDA, ROCm, profiling tools).
What Success Looks Like
- Measurable gains in latency, throughput, and cost efficiency .
- Optimized inference systems running reliably in production.
- Research ideas successfully translated into deployable systems.
- Clear benchmarks and documentation that inform product decisions.
- Relevant Research Areas (Bonus)
Long-context inference optimization
Speculative decoding
KV-cache compression and paging
Efficient decoding strategies
Hardware-aware inference design
Originally posted on Himalayas
