GPU Engineer

GPU Engineer
New York (or Remote)
$150 – 200K
Can Sponsor
   
We are delighted to be working exclusively with a well-funded AI-Drug Discovery company that are on the lookout for a top GPU Engineer. The company boast immense computational power and resources for their size and have serious growth plans over the next twelve months, this is the perfect time to join and make a big impact!
  
The company embrace flexibility, and this is evident with employees working from all over the North America and Europe. Collaboration is deeply engrained into the culture, and you will be working closely with a talented team of computational scientists, software engineers, ML engineers.
  
This role will place you at the forefront of scientific innovation, leveraging your expertise in optimizing GPU memory usage, and implementing parallelization and scaling on the cloud. Your primary focus will be on developing and fine-tuning scientific code for deep learning, with a strong emphasis on PyTorch and low-level GPU programming.
  
Working in collaboration with machine learning teams, you will design and implement software flows that seamlessly accelerate machine learning applications, address end-user integration needs, and deliver peak performance without compromising quality.
  
If you have experience working with large diffusion model architectures or geometric deep learning, with a demonstrated track record in performance optimization and MLOps, this is an opportunity to accelerate your career to the next level whilst making a profound impact on designing and developing better drugs for the future.
  
Ideally, you will have:

• Deep familiarity with deep learning frameworks, such as PyTorch, Tensorflow, Jax, ONNX
• Extensive experience with high performance deep-learning workload GPU profiling and runtime optimization
• Experience working on performance optimization of GPU kernels or mapping of deep learning primitives onto custom accelerator architectures
• 1 year + industry or post-graduate experience with deep learning algorithms, understanding of deep learning architectures such as MLPs, CNNs, RNNs, Transformers
• Ability to break apart existing code; solve ad hoc problems with little help
• No task too big or small; strong sense of independence and ownership
• Provide guidance and opinions on implementation and considerations
• Experience working on drug discovery projects (desired, not a requirement)

We can be flexible on the level and salary for this vacancy, if you fall outside of the advertised salary bandings please still apply and we can address this at our first touchpoint.
  
Interview Process:

1. Intro call with Bloom Life Science
2. Intro call with hiring manager (Zoom)
3. Panel call with other senior team members (Zoom)
4. Technical Exercise + Presentation

KEYWORDS: GPU Engineer, Parallel Computing, GPU Optimization, Deep Learning, GPU Architecture, CUDA, Drug Discovery, Biotechnology, Artificial Intelligence, Machine Learning