Senior Data Scientist

6 hours ago

Apply Now

Receive Emails with Similar Jobs

Calix

WebsiteLinkedInAll Job Openings

GPON VDSL2 ADSL2+ Metro-Ethernet VoIP IPTV FTTH MSAP MSAN H.248 GR-303 10GE

1001 - 5000

💰 $50M Venture Round on 2009-08

Description

• This is a remote-based position that can be located anywhere in the United States or Canada. • Develop Advanced Generative Models: Research, design, and implement generative models (e.g., GPT, GANs, VAEs, Transformer architectures) for a variety of tasks, including text generation, image synthesis, and creative AI applications. • Define platform requirements: Formulate, design, and build platform components. • Experimentation and Prototyping: Design and conduct experiments to explore new algorithms and approaches for enhancing generative model performance. • Prototype models and test them against established benchmarks. • Model Training and Optimization: Lead the training and fine-tuning of large-scale AI models, optimizing them for specific use cases. • Use advanced techniques such as transfer learning, reinforcement learning, and self-supervised learning to enhance performance. • Data Analysis and Feature Engineering: Collaborate with data engineers and analysts to preprocess large datasets, extract features, and create high-quality training data for generative AI models. • Ensure data quality and integrity throughout the pipeline. • Performance Evaluation and Iteration: Evaluate model outputs using metrics such as FID, BLEU, or perplexity, and iteratively refine models based on quantitative feedback. • Use statistical methods and A/B testing to assess model performance in production environments. • Collaborate Across Teams: Work closely with machine learning engineers, software developers, and AI researchers to integrate models into production systems. • Help define the technical roadmap and prioritize tasks aligned with business objectives. • Drive Research and Innovation: Stay up-to-date with the latest research in generative AI, machine learning, and deep learning, and apply new advancements to improve models and systems. • Mentorship and Leadership: Provide technical mentorship and guidance to junior data scientists and machine learning engineers, fostering a culture of innovation, collaboration, and continuous learning.

Requirements

• Bachelor’s, Master’s, or Ph.D. in Data Science, Computer Science, Machine Learning, Statistics, or a related field. • 5+ years focus on delivering use cases or core AI/ML platform components. • 8+ years focus on quantitative, analytics work with data. • Solid background in Quantitative and Advanced mathematics: Applied Mathematics, Quantitative Economics, Statistics. • Experience working with domain experts and translate knowledge and requirements into AI products. • Strong background applying Statistical Learning to interpret, evaluate and optimize outcomes and familiar with metrics and their applications to use cases. • Experience preparing and synthetic datasets for training, testing and validation. • Proven experience with deep learning models, including transformer architectures, GANs, VAEs, or similar generative techniques. • Strong track record in building, training, and deploying machine learning models for real-world applications. • Expertise in Python and deep learning frameworks such as TensorFlow, PyTorch, or Keras. • Strong understanding of NLP techniques (e.g., GPT, BERT) and/or computer vision models (e.g., GANs, VAEs). • Experience with data processing, feature engineering, and working with large-scale datasets. • Proficiency with SQL and experience with data pipeline tools (e.g., Apache Spark, Hadoop, Airflow). • Familiarity with MLOps practices for model deployment and monitoring. • Privacy and compliance, worked on large scale data, deployment and maintaining models in production, experience implementing/optimizing ML algorithms. • Strong problem-solving skills and ability to work in a fast-paced, iterative environment. • Excellent communication skills, both written and verbal, with the ability to present technical concepts to non-technical stakeholders. • Proven leadership skills and ability to mentor and guide team members. • Experience with multimodal models (text, image, video) in generative AI applications. • Familiarity with Reinforcement Learning, Self-Supervised Learning, or Few-shot Learning. • Knowledge of cloud computing platforms (AWS, GCP, Azure) and experience deploying models in cloud environments. • Experience publishing in AI/ML conferences or journals, and active participation in AI research communities. • GenAI: RAG pipeline components, LLM pre-training, alignment, fine tuning, different types of LLM and their applications.