About the Team
Our team plays a pivotal role in enhancing the capabilities and efficiency of our advanced AI systems. We design solutions that enable our machine learning models to scale seamlessly and perform optimally in real-world applications and large-scale research. Collaborating across InstaDeep, we directly impact projects in diverse fields including Life Sciences, Logistics, Chip Design, and Quantum ML.
The Role
We seek a highly skilled Machine Learning Engineer with a passion for tackling the challenges of large-scale ML development. You'll play a vital role in making our ambitious AI solutions a practical reality. If you thrive on system-level analysis, find joy in squeezing every ounce of performance from hardware, and love diving deep into algorithm optimisation, this is the position for you.
Responsibilities
- Scaling Expertise: Design and implement strategies to efficiently scale machine learning models across diverse hardware platforms (GPU/TPU).
- Performance Optimization: Analyse and profile ML systems under heavy load, pinpointing bottlenecks, and implementing targeted optimizations.
- Distributed Systems Architecture: Create robust distributed training and inference solutions for maximum computational efficiency.
- Algorithmic Optimization: Research and understand the latest deep learning literature to implement and optimize state-of-the-art algorithms and architectures, ensuring compute efficiency and performance.
- Low-Level Mastery: Write high-quality Python, C/C++, XLA, Pallas, Triton, and/or CUDA code to achieve performance breakthroughs.
Required Skills
- Understanding of Linux systems, performance analysis tools, and hardware optimisation techniques.
- Experience with distributed training frameworks (Ray, Dask, PyTorch Lightning, etc.).
- Expertise with Python and/or C++.
- Development with machine learning frameworks (JAX, Tensorflow, PyTorch etc.).
- Passion for profiling, identifying bottlenecks, and delivering efficient solutions.
Highly Desirable
- Track record of successfully scaling ML models.
- Experience writing custom CUDA kernels or XLA operations.
- Understanding of GPU/TPU architectures and their implications for efficient ML systems.
- Fundamentals of modern Deep Learning.
- Actively following ML trends and a desire to push boundaries.
Example Projects
- Profile algorithm traces, identifying opportunities for custom XLA operations and CUDA kernel development.
- Implement and apply SOTA architectures (MAMBA, Griffin, Hyena) to research and applied projects.
- Adapt algorithms for large-scale distributed architectures across HPC clusters.
- Employ memory-efficient techniques within models for increased parameter counts and longer context lengths.
What We Offer
- Real-World Impact: Directly contribute to the performance and reach of our AI solutions.
- Cutting-Edge Challenges: Tackle complex problems at the forefront of machine learning and large-scale system design.
- Growth-Oriented Environment: Expand your expertise in a team of talented engineers dedicated to advancing ML scalability.
Benefits Extracted with AI
- Real-World Impact
- Cutting-Edge Challenges
- Growth-Oriented Environment
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