Lead Machine Learning Engineer
Hybrid
Full Time
#Research
#Python
#C C++
#Linux Systems
#Performance Analysis
#Machine Learning
#Jax
#TensorFlow
#PyTorch
#CUDA
Founded in 2014, InstaDeep is a pioneering force in the artificial intelligence industry. With a global presence spanning cities like London, Paris, Berlin, and San Francisco, we collaborate with world-class partners such as Google DeepMind and prestigious academic institutions including Oxford, Stanford, and MIT. As a Google Cloud Partner and an NVIDIA Elite Service Delivery Partner, we have been recognized by the Financial Times and Statista as one of Europe’s fastest-growing companies. Following our acquisition by BioNTech, we remain dedicated to pushing the boundaries of AI. We are currently looking for a Lead Machine Learning Engineer to join our team in the United Kingdom on a full-time, hybrid basis.
Key outcomes
- Define our long-term technical roadmap and lead the development of high-performance, scalable machine learning systems.
- Optimize state-of-the-art deep learning architectures to ensure maximum compute efficiency.
- Design robust strategies for scaling models across diverse hardware platforms, including GPUs and TPUs.
- Develop efficient code using Python, C/C++, XLA, Pallas, Triton, or CUDA to drive performance breakthroughs.
- Architect distributed systems for training, monitoring, and deployment to handle large-scale datasets.
- Create automated data pipelines to streamline model validation and deployment.
- Collaborate across research and product teams to build a cohesive and effective software stack.
- Mentor our engineering team by fostering excellence in documentation, testing, and coding standards.
Requirements
- Expertise in Python and C/C++.
- A deep understanding of Linux systems, hardware optimization, and performance analysis tools.
- Proven experience working with major machine learning frameworks such as JAX, TensorFlow, or PyTorch.
- A strong grasp of modern deep learning fundamentals.
- A demonstrated passion for profiling systems, identifying bottlenecks, and delivering high-impact technical solutions.
- The legal right to work in the United Kingdom.
Preferred qualifications
- A proven track record of successfully scaling complex machine learning models.
- Experience in writing custom CUDA kernels or XLA operations.
- Comprehensive knowledge of GPU and TPU architectures and how they influence system efficiency.
Compensation
We offer a dynamic work environment where you can lead talented engineers in solving some of the most challenging system problems in the field of AI. You will gain hands-on experience optimizing large-scale distributed systems that support industry-leading research. Our team benefits from a hybrid work model, which includes working from the office three days per week to foster close collaboration and innovation.
How to apply
If you are ready to help shape the future of artificial intelligence and lead our engineering efforts, we invite you to submit your application. We are committed to fostering an authentic and diverse workplace and encourage applications from all backgrounds.
