Solutions Architect - AI for Drug Discovery

NVIDIA NVIDIA · Semiconductors · United Kingdom +5 · Remote

NVIDIA seeks a Solutions Architect for their EMEA team to drive AI adoption in drug discovery within the biopharma industry. The role involves acting as a technical advisor to pharmaceutical companies, biotechs, and research organizations, leveraging NVIDIA's computing platform. Responsibilities include building proof-of-concept demonstrations, scaling AI deployments, and supporting business development by guiding customers on production-grade inference, model training, RL, and post-training algorithms. The role also involves exploring foundation models, agentic LLM applications, and physical AI in biopharma, providing feedback to internal teams, and documenting/teaching NVIDIA solutions.

What you'd actually do

  1. Support Business Development and Sales teams as part of a Team of 4, partnering with Industry Business leads, Account Managers, and Developer Relations managers to drive our developers’ ecosystem success.
  2. Work directly with developers and customers in a customer-facing setting
  3. Guide customers in implementing production grade inference deployments, model training, reinforcement learning (RL), and post-training algorithms by helping them adopt NVIDIA AI SDKs and APIs.
  4. Analyze application architectures and find opportunities for acceleration.
  5. Document what you know and teach others e.g. by building targeted trainings for partners and other Solutions Architects, delivering hackathons and technical demonstrations on NVIDIA solutions and platforms, writing whitepapers or blogs, or simply working through hard problems with a customer on a whiteboard.

Skills

Required

  • MS or PhD (or equivalent experience) in Computer Science, Computational Biology, Computational Chemistry or Biomedical Engineering with strong applied experience in these domains.
  • 5+ years of experience in software development related to AI/ML in healthcare or life sciences.
  • Proficiency in Python and AI/ML frameworks (PyTorch, Langchain, or building custom framework).
  • Experience developing, training and customizing Transformer models for healthcare and life sciences applications, especially using libraries like Transformer Engine or Megatron-LM.
  • Excellent communication skills with the ability to present complex technical concepts to both technical and non-technical audiences.
  • Must enjoy interacting with forward-thinking people, life-long learning, and staying at the forefront of the domain

Nice to have

  • Background with accelerating scientific algorithms using parallel programming (e.g., using CUDA), or experience with distributed programming models for supercomputing applications, AI deployment/inference technologies (e.g. TensorRT) or optimization frameworks (e.g. cuOpt), is a plus.
  • Experience with large-scale pre-training and/or post-training of Transformer-based architectures for language, vision and/or multiple modalities.
  • Experience deploying and scaling agentic AI solutions in cloud environments (AWS Bedrock, Azure AI foundry, Vertex AI, etc).
  • Experience developing deep learning models using clinical trial or real‑world patient data.
  • Hands-on experience with AI for physical systems, such as robotic instruments, autonomous labs, or intelligent devices, or with NVIDIA platforms (Metropolis, Cosmos, Omniverse, Isaac, CUDA-X)
  • Experience in the pharmaceutical industry or established thought leadership through publications or presentations on AI/ML applications in healthcare and life science.

What the JD emphasized

  • production grade inference deployments
  • model training
  • reinforcement learning (RL)
  • post-training algorithms
  • Transformer models
  • large-scale pre-training
  • post-training of Transformer-based architectures
  • agentic AI solutions

Other signals

  • customer-facing
  • production grade inference deployments
  • model training
  • reinforcement learning
  • post-training algorithms
  • NVIDIA AI SDKs and APIs
  • application architectures
  • foundation model training and customization
  • agentic LLM applications
  • physical AI applications
  • Transformer models
  • Transformer Engine
  • Megatron-LM
  • large-scale pre-training
  • post-training of Transformer-based architectures
  • agentic AI solutions
  • AI for physical systems
  • NVIDIA platforms