Senior Machine Learning Engineer - AI & GPU Performance job opportunity at Synthesia.

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Synthesia Senior Machine Learning Engineer - AI & GPU Performance
Experience: 4-years
Pattern: hybrid
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degreePhD
loacation Amsterdam; Copenhagen; London; Munich; UK; Zurich, Netherlands
loacation Amsterdam; Cop..........Netherlands

As a #ML #Performance #Engineer in the #AI & #GPU Performance team you will contribute to the design and development of high performance solutions. You will join a team of 40+ #Researchers and #Engineers within the R&D Department working on cutting edge challenges in the Generative AI space, with a focus on creating highly realistic, emotional and life-like Synthetic humans through text-to-video. Within the team you’ll have the opportunity to work on the applied side of our research efforts and directly impact our solutions that are used worldwide by over 60,000 businesses. This is an opportunity to work for a company that is impacting businesses at a rapid pace across the globe. __ What will you be doing? As a ML Performance Engineer in the AI & GPU Performance team you will contribute to the design and development of high performance solutions. You will own one or more projects for computationally optimizing large-scale model training and inference pipelines. By partnering with researchers and research teams you’ll identify high-impact initiatives and push the boundaries of model performance. You will work on re-implementing models in an efficient manner by using #PyTorch and underlying technologies like CUDA/Triton, Torch compilation, etc. __ This would include: Evaluating, profiling and optimising compute resource usage (e.g., Hopper & Blackwell GPUs) for cost and time efficiency at training and inference times Developing customized efficient solutions for inference pipelines (CUDA/Triton kernels) as well as Introducing or enhancing tooling for achieving optimal computational performance (e.g. DL compilers, ONNX, TensorRT) Driving the adoption of best practices for large-model training, including checkpointing, gradient accumulation, and memory optimisation among others Introducing or enhancing tooling for distributed training, performance monitoring, and logging (e.g., DeepSpeed, PyTorch Distributed) Designing and implement techniques for model parallelism, data parallelism, and mixed-precision training Keeping updated on the latest research in model compression (e.g., quantization, pruning) and advanced optimisation methods __ Who are you? You are a ML engineer passionate about high performance computing You have a background in Computer Science / Engineering and 3+ years of industry experience. (PhD preferred) You have worked on optimising large models for over 2 years You have experience developing CUDA/Triton kernels and optimizing models with DL compilers (torch.compile) You have great coding skills in Python and C++ and you care about writing clean, and efficient code You have experience with optimising distributed systems and distributed tools like DDP, Deepspeed, Accelerate or similar You have some experience in the video space (Diffusion models / GAN’s) You are interested in doing research, trying new things and pushing the boundaries, going beyond what's already known __ The good stuff... Attractive compensation (salary + stock options + bonus) Private Health Insurance in London Hybrid work setting with an office in London 25 days of annual leave + public holidays Work in a great company culture with the option to join regular planning and socials at our hubs. A generous referral scheme when you know people that are amazing for us Strong opportunities for your career growth

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