Senior Applied Scientist, Efficient LLM Inference & Model Optimization

United States Digital Space LLC; Greater London; On-site; GBP 120,000 - 160,000; Full time

Benefits offered by this job

Competitive compensation

Career growth and learning

Flexibility and ownership

Collaborative and innovative culture

Impactful AI projects

International environment

Job summary

Token Factory is seeking a Senior Applied Scientist to turn frontier inference bottlenecks into credible research and production-ready solutions. You will design rigorous experiments, write strong code, and collaborate with engineers to ship results.

You will own well-scoped research projects, publish or prepare high-quality technical work, and prototype components that engineers can productionize. A strong research background and coding ability are essential for impact.

Qualifications

• PhD in CS/ML or closely related field.
• Strong publication record or equivalent in ML/efficient inference.
• Hands-on coding in Python and PyTorch; move from idea to experiment quickly.
• Deep understanding of LLMs, VLMs, transformer inference, and production-serving tradeoffs.
• Strong experimental design skills with ablations, baselines, metrics and analysis.
• Excellent written and verbal communication.

Responsibilities

• Own focused research projects from hypothesis through production handoff.
• Publish internal reports, blogs, or papers when credible externally.
• Collaborate with MLEs to productionize research prototypes.
• Define research programs in efficient LLM and VLM inference with measurable impact.
• Build prototypes in PyTorch, Triton, CUDA-adjacent tooling; work with engineers to productionize.
• Design evaluation methodology covering quality, latency, memory, and cost per token.
• Publish open-source artifacts and technical reports for external credibility.
• Collaborate with cross-functional teams to choose high-leverage research bets.
• Mentor engineers and scientists on experimental design and tradeoffs.

Skills

Python

PyTorch

LLM inference

Experimental design

Publication track record

Technical writing

Communication

Education

PhD in CS / ML / ML systems

Tools

CUDA

Triton

NVIDIA TensorRT

Model compression / quantization tooling

Job description

About the company

the company is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure.

Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI.

Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D.

The role

the company Token Factory needs scientists who can turn frontier inference bottlenecks into research problems, publish credible work, and then help ship the results into production. This is not a papers-only research role. The Applied Scientist is expected to design rigorous experiments, write strong code, collaborate with engineers, and convert research into deployed inference capabilities.

A Senior Applied Scientist owns well-scoped research and production optimization projects. They can publish or prepare high-quality technical work while also producing code, experiments, and prototypes that engineers can use.

Your responsibilities

• Own focused research projects from hypothesis through experiment, ablation, prototype, and production handoff.
• Prepare internal reports, technical blogs, or papers when the work is externally credible.
• Partner directly with MLEs to ensure research prototypes become usable production components.
• Define and execute research programs in efficient LLM and VLM inference with measurable production impact.
• Invent, evaluate, and productionize methods for quantization, QAT, distillation, speculative decoding, KV-cache reuse, KV-cache compression, long-context inference, MoE routing, and model/runtime co-optimization.
• Build high-quality prototypes in PyTorch, Triton, CUDA-adjacent tooling, or inference-serving frameworks, then work with MLEs and platform engineers to productionize them.
• Design rigorous evaluation methodology covering quality, latency, throughput, numerical stability, memory footprint, tail latency, and cost per token.
• Publish papers, technical reports, blog posts, and open-source artifacts that build external credibility for the company Token Factory.
• Collaborate with MLE, GPU kernel, backend infrastructure, product, and customer teams to choose high-leverage research bets.
• Mentor engineers and scientists on experimental design, scientific rigor, and model/system tradeoffs.

Must-haves

• PhD in computer science, machine learning, ML systems, computer systems, computer architecture, electrical engineering, applied math, or a closely related field.
• Strong publication record or equivalent research artifacts in ML, ML systems, efficient inference, model compression, quantization, distillation, serving systems, or related areas.
• Strong hands-on coding ability in Python and PyTorch; ability to move from idea to experiment to prototype quickly.
• Deep understanding of LLMs, VLMs, transformer inference, decoding algorithms, model compression, quantization, and production-serving tradeoffs.
• Strong experimental design skills, including ablations, baselines, metrics, statistical reasoning, and failure analysis.
• Excellent written and verbal communication.

Nice-to-haves

• First-author publications in NeurIPS, ICML, ICLR, MLSys, ACL, EMNLP, ASPLOS, OSDI, SOSP, ISCA, HPCA, or comparable venues.
• Experience deploying ML models or inference optimizations in production.
• Experience with vLLM, SGLang, TensorRT-LLM, NVIDIA Dynamo, FlashAttention, FlashInfer, Triton, CUDA, or PyTorch internals.
• Experience with post-training, SFT, DPO, RLHF, RLAIF, preference optimization, or synthetic data generation when connected to inference quality or efficiency.
• Open-source research artifacts, widely used benchmarks, high-quality technical blogs, or invited talks in efficient AI systems.

Benefits & Perks

• Competitive compensation
• Career growth and learning opportunities
• Flexibility and ownership
• Collaborative and innovative culture
• Opportunity to work on impactful AI projects
• International environment and talented teams

What's it like to work at the company

Fast moving- Bold thinking- Constant growth- Meaningful impact- Trust and real ownership- Opportunity to shape the future of AI

Equal Opportunity Statement

the company is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law.

Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire.

If you need accommodations during the application process, please let us know.

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