At a Glance
- Tasks: Build and optimise simulation models for large-scale ML systems and run real-world experiments.
- Company: Join a cutting-edge, research-driven organisation at the forefront of ML infrastructure.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on hands-on technical challenges and career advancement.
- Why this job: Make a significant impact on ML architecture decisions and work with innovative technologies.
- Qualifications: Master's or PhD in relevant fields with strong ML systems and performance engineering background.
The predicted salary is between 59400 - 72600 £ per year.
We're partnering with a well-funded, research-driven organisation at the frontier of large-scale ML infrastructure. This is a hands-on technical role for someone who enjoys going deep on performance modelling, distributed systems, and real hardware behaviour — with direct influence over architecture decisions at scale.
What you'll do:
- Build simulation models for compute, memory, interconnect, and communication behaviour across large-scale ML systems
- Develop tools to simulate training and inference workloads across distributed accelerator clusters
- Model distributed execution patterns including collectives, synchronisation, and communication bottlenecks
- Run experiments and benchmarks on real ML systems to calibrate and validate simulation models
- Analyse end-to-end performance: throughput, latency, scaling efficiency, and cost/performance tradeoffs
- Collaborate with hardware, software, networking, and ML teams to communicate findings through design recommendations
What we're looking for:
- Master's or PhD in CS, Electrical or Computer Engineering, or related field
- Strong background in ML systems, distributed systems, performance engineering, or simulation
- Experience analysing compute, communication, and memory behaviour in large-scale ML systems
- Hands-on benchmarking, profiling, and measurement of ML systems
- Familiarity with distributed training concepts: data/tensor/pipeline parallelism, collectives, synchronisation
- Proficiency in Python, C++, or Rust
To find out more please reach out to Charles Duran.
Senior ML Systems Engineer in London employer: Intellectual Capital Resources
Join a dynamic and innovative audio AI product company that prioritises employee growth and collaboration in a fully remote environment. With a strong focus on cutting-edge technology and a culture of mentorship, you'll have the opportunity to work alongside talented professionals while contributing to impactful projects that enhance sound experiences globally. Enjoy the flexibility of remote work, competitive benefits, and a commitment to fostering a supportive and inclusive workplace.
Contact Details:
Intellectual Capital Resources Recruitment Team
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