ML Scientist Foundation Model

Machine Learning Scientist - Foundation Models

About Egen:

Egen is a rapidly growing data-first company leveraging advanced technology platforms to drive impactful decisions through data and insights.

Job Description:

We are seeking a talented Machine Learning Scientist to join our team and contribute to the development of cutting-edge foundation models. You will play a key role in building, optimizing, and evaluating large multimodal models across various data modalities.

Responsibilities:

• Design, develop, and optimize large multimodal models.
• Collaborate with engineering and research teams to establish evaluation pipelines and enhance task-specific model performance.
• Implement model training, profiling, inference, and fine-tuning using state-of-the-art tools and methodologies.
• Develop scalable ML evaluation pipelines and multimodal model training and data pipelines.

Qualifications:

• Bachelor's or Master's degree in Computer Science, Physics, Mathematics, or a related field.
• Strong Python programming skills.
• Proven experience in neural network model development, including building or fine-tuning large models.
• Expertise in model performance profiling and optimization.
• Experience with developing scalable ML evaluation pipelines.
• Familiarity with multimodal model training and data pipeline development.

Preferred Qualifications:

• Experience with Transformer, Diffusion, Graph, Contrastive, or Genetic models.
• ML platform engineering experience at scale on GCP.
• Experience designing and implementing evaluation frameworks.
• Experience working in production environments with distributed training or inference.

Benefits:

Egen offers a competitive salary and comprehensive benefits package, including health insurance, paid leave, 401(k) employer match, and employee referral bonuses.

Qualifications

Required:

• Bachelor's or Master's degree in Computer Science, Physics, Mathematics, or a related field.
• Proficiency in Python programming.
• Demonstrated experience in developing and fine-tuning neural network models, including experience with large-scale models.
• Strong understanding of model performance profiling and optimization techniques.
• Ability to design and implement scalable machine learning evaluation pipelines.
• Familiarity with multimodal model training and data pipeline development.

Preferred:

• Experience with Transformer, Diffusion, Graph, Contrastive, or Genetic models.
• Experience with ML platform engineering at scale on Google Cloud Platform (GCP).
• Proven ability to design and implement evaluation frameworks.
• Experience working in production environments with distributed training or inference.

Key Responsibilities

• Model Development & Optimization: Design, build, and optimize large-scale multimodal foundation models across diverse data modalities (text, image, audio, etc.).
• Performance Evaluation & Improvement: Collaborate with engineering and research teams to establish robust evaluation pipelines and drive continuous performance enhancements for task-specific applications.
• Model Training & Deployment: Implement efficient training, profiling, inference, and fine-tuning strategies for foundation models using cutting-edge tools and methodologies.
• Pipeline Development: Develop scalable machine learning (ML) evaluation pipelines and contribute to the design and implementation of multimodal model training and data pipelines.
• Research & Innovation: Stay abreast of the latest advancements in foundation model research and explore novel techniques to enhance model capabilities and efficiency.

Egen - ML Scientist Foundation Model Hiring Workflow

1. Application Review: Candidates submit resumes and cover letters through the Egen job portal. The AI-powered system screens applications based on keywords, skills, and experience matching the job description.

2. Initial Screening: Qualified candidates are invited for a 30-minute phone interview with a recruiter to discuss their background, experience, and motivation for the role.

3. Technical Assessment: Shortlisted candidates complete a technical assessment evaluating their Python programming skills, understanding of machine learning concepts, and ability to solve practical problems related to model development and optimization.

4. Team Interview: Top performers participate in a panel interview with the hiring manager and team members. This stage focuses on assessing their technical expertise, problem-solving abilities, communication skills, and cultural fit within Egen.

5. Final Decision: The hiring team reviews all interview feedback and technical assessment results to make a final decision.

6. Offer & Onboarding: Selected candidates receive a formal job offer outlining compensation and benefits. Successful onboarding includes introductions to the team, project assignments, and comprehensive training on Egen's tools and technologies.

About Egen

Egen is a rapidly expanding data-driven company dedicated to transforming data into actionable insights and impactful solutions. We leverage cutting-edge technology platforms to empower our clients with the knowledge they need to make informed decisions and drive positive change.

At Egen, we believe in the power of data to unlock new possibilities. Our team of passionate experts combines technical expertise with a deep understanding of business needs to deliver innovative and effective solutions. We are committed to fostering a collaborative and inclusive work environment where creativity and innovation thrive.