FREE ACCESS
5,000–10,000 jobs/day
See all jobs on Scoutfield
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in training and evaluating large-scale foundation models, particularly in the context of materials science and chemistry. Proficient in software engineering best practices to build scalable systems and integrate multi-modal data effectively.
Highest-signal resume keywords
Large-Scale Model TrainingMulti-Modal Foundation ModelsSoftware Engineering Best PracticesRepresentation LearningPhD in CS, ML, or Related Field
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Large-Scale VisionLLM Pre-TrainingReinforcement LearningSelf-Supervised LearningContrastive Pre-TrainingData CurationModel Evaluation DesignGPU Parallel TrainingScientific Data HandlingEmbedding Learning
Soft Skills
Collaboration with ScientistsInterest in World-Changing ChallengesWillingness to Learn
Tools & Technologies
Distributed SystemsHigh-Performance ComputingMulti-Modal Training Frameworks
Industry Keywords
Materials ScienceExperimental ChemistryComputational WorkflowsData Scaling StudiesScientific Datasets
About the role
Key responsibilities & impact- Train and evaluate foundation models for CuspAI’s materials discovery platform
- Build and train specialised foundation models on terabytes of scientific data at massive scale
- Enhance the platform’s multi-modal capabilities across crystal structures, properties, text, and images
- Design and run scaling studies, ablations, and evaluations to determine effective modelling choices
- Lead or contribute to parallel training of multi-modal foundation models across hundreds of GPUs
- Handle scientific datasets in the order of terabytes
- Apply software engineering best practices to create scalable, reliable, and maintainable systems
- Own data curation and evaluation design for models
- Partner with chemists and materials scientists to integrate computational and experimental workflows into a unified optimisation loop
- Learn and use the technical vocabulary of materials science and experimental chemistry
- Align system designs with CuspAI’s sustainability and world-changing mission
- Own models end to end, from data and experiment design through large-scale training and integration
Requirements
What you’ll need- Personal interest in enabling scientists to work on world-changing challenges and in CuspAI’s technology
- Strong software engineering skills and a proven track record building complex, high-performance systems at scale in a production or industrial environment
- Hands-on experience training or evaluating large models in a distributed setting
- Senior candidates: experience leading a multi-hundred-GPU training effort
- Earlier-career candidates: experience as a core contributor to a multi-hundred-GPU training effort
- Depth in at least one of large-scale vision or representation learning, LLM pre-training or post-training, or reinforcement learning
- Deep interest in the connection between AI and materials science or chemistry
- Willingness to rapidly learn materials science and experimental chemistry vocabulary
- Master’s or PhD in CS, ML, or a related quantitative field, or equivalent experience
- Experience with representation learning and computer vision, including self-supervised or contrastive pre-training, learning embeddings, or extracting structured information from images such as figures, plots, and diagrams
- Typically 1 to 3 years at a frontier lab or equivalent for strong early-career research engineers
- Bonus: RL for post-training, including RLHF or GRPO-style methods, or RL in agentic settings
- Bonus: Experience at a frontier lab or a team that ships models at scale
- Bonus: Prior machine learning application to materials science, chemistry, or molecular informatics
- Bonus: Exposure to multi-modal training across text, vision, and other modalities
- Existing right to work or visa eligibility for the selected location
Benefits
Comp & perks- Equity in CuspAI
- 28 days holiday (DE, NL, UK) or 21 days holiday (JP, SG, US), in addition to local public holidays
- 26 weeks parental leave for primary caregivers at full pay
- 12 weeks parental leave for secondary caregivers at full pay
- Professional development budget
- Opportunity to work on meaningful materials science, sustainability, and climate-related problems
- Interdisciplinary teamwork with AI researchers, computational chemists, experimental scientists, and world-class researchers and engineers
- Reasonable adjustments during or after the interview process