ML Scientist - Foundation Model

About Egen: 
Egen is a fast-growing and entrepreneurial company with a data-first mindset. We bring together the best engineering talent working with the most advanced technology platforms, including Google Cloud and Salesforce, to help clients drive action and impact through data and insights. We are committed to being a place where the best people choose to work so they can apply their engineering and technology expertise to envision what is next for how data and platforms can change the world for the better. We are dedicated to learning, thrive on solving tough problems, and continually innovate to achieve fast, effective results. If this describes you, we want you on our team.
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About the opportunity:  In this role, you'll join a core team focused on building and scaling foundation models across a variety of modalities. You will work on training large multimodal models and fine-tuning checkpoints to domain-specific tasks. The team is responsible for the full lifecycle of model development, from data pipeline design and training infrastructure to evaluation, optimization, and deployment into production environments. This role is ideal for someone who thrives at the intersection of model research and production implementation, and who is excited to contribute to the next generation of scalable AI systems.

Responsibilities:

• You will work closely with engineering and research teams to build and optimize models, develop evaluation pipelines, and drive task-specific model performance improvements. Your work will span training, profiling, inference, and fine-tuning, using state-of-the-art tools and methodologies across supervised and unsupervised domains.

Basic Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Physics, Mathematics, or a related field
• Python programming
• Neural network model development, including experience building or fine-tuning large models (e.g., recommendation systems)
• Model performance profiling and optimization
• Developing scalable ML evaluation pipelines
• Multimodal model training and data pipeline development
• Task-specific fine-tuning and distillation methods

Preferred Qualifications:

• The ideal candidate will also have experience in one or more of the following:
• Training or experimentation with Transformer, Diffusion, Graph, Contrastive, or Genetic models
• ML platform engineering at scale, preferably on GCP
• Evaluation framework design and implementation
• Experience working in production environments with distributed training or inference

Personal Attributes:

• Strong technical foundation in machine learning and systems
• Demonstrated ownership of complex ML development workflows
• Collaborative mindset and comfort working across teams
• Curiosity and enthusiasm for exploring a wide range of ML architectures and training techniques
• Excellent communication and documentation skills

Compensation & Benefits:
This role is eligible for our competitive salary and comprehensive benefits package to support your well-being:- Comprehensive Health Insurance- Paid Leave (Vacation/PTO)- Paid Holidays- Sick Leave- Parental Leave - Bereavement Leave- 401 (k) Employer Match- Employee Referral Bonuses
Check out our complete list of benefits here - >https://egen.ai/people/#benefits
Important: All roles are subject to standard hiring verification practices, which may include background checks, employment verification, and other relevant checks.
EEO and Accommodations:
Egen is an equal opportunity employer and is committed to inclusion, diversity, and equity in the workplace. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veterans’ status, or any other characteristic protected by federal, state, or local laws. Egen will also consider qualified applications with criminal histories, consistent with legal requirements. Egen welcomes and encourages applications from individuals with disabilities. Reasonable accommodations are available for candidates during all aspects of the selection process. Please advise the talent acquisition team if you require accommodations during the interview process.