Applied Scientist

Culver, CA - USA

Monthly Salary: Not Disclosed

Posted on: 29 days ago

Vacancies: 1 Vacancy

Job Summary

The Applied Scientist will work on designing developing and implementing sophisticated machine learning and AI models to solve complex product engineering and business problems. The role involves building end-to-end ML pipelines developing AI tools and APIs and collaborating closely with engineering product and marketing partners to bring intelligent data-driven and AI-powered solutions into production. The ideal candidate combines deep technical expertise in machine learning statistical modeling and AI framework development with strong problem-solving and interpersonal skills ensuring effective collaboration and measurable impact in a fast-paced environment.

PhD in Statistics Computer Science Mathematics or a related quantitative field with 1 year of relevant experience; or MS with 4 years of experience in applied machine learning statistical modeling or AI development.
1 years experience and programming proficiency with Python. Alternatively we may consider C/C Java etc.
1 years experience and working proficiency with distributed systems: Hadoop Spark etc.
1 years experience and working proficiency with statistical modeling and machine learning algorithms for supervised and unsupervised learning including classification regression clustering etc.
Working familiarity with causal inference models.
Working familiarity with deep learning algorithms: CNN/RNN/LSTM/Transformer etc and deep learning frameworks like TensorFlow or PyTorch.
Knowledge of model evaluation validation techniques and performance metrics.
Ability to translate research ideas into scalable production-level ML/AI solutions.
Excellent analytical communication and collaboration skills across multi-functional teams.
Hands-on experience in programming and implementing ML/AI models in Python or similar languages.
Experience in building and maintaining machine learning pipelines including data preprocessing model training and deployment.
Demonstrated ability to develop and apply statistical and machine learning models for prediction optimization or causal analysis.
Experience developing AI tools frameworks or APIs to support model deployment or LLM-based applications.

Experience with LLM fine-tuning prompt engineering or retrieval-augmented generation (RAG).
Familiarity with generative AI techniques (e.g. diffusion models transformer architectures).
Experience building scalable ML systems using cloud platforms (AWS GCP or Azure) and ML Ops tools (e.g. SageMaker Vertex AI MLflow).
Understanding of data engineering and feature pipeline automation.
Experience developing or contributing to AI frameworks APIs or internal tools used by other teams.
Strong software engineering practices including version control testing and code review.
Familiarity with cross-domain applications of AI/ML (e.g. marketing analytics personalization recommendation systems).

PhD in Statistics Computer Science Mathematics or a related quantitative field with 1 year of relevant experience; or MS with 4 years of experience in applied machine learning statistical modeling or AI development.
1 years experience and programming proficiency with Python. Alternatively we may consider C/C Java etc.
1 years experience and working proficiency with distributed systems: Hadoop Spark etc.
1 years experience and working proficiency with statistical modeling and machine learning algorithms for supervised and unsupervised learning including classification regression clustering etc.
Working familiarity with causal inference models.
Working familiarity with deep learning algorithms: CNN/RNN/LSTM/Transformer etc and deep learning frameworks like TensorFlow or PyTorch.
Knowledge of model evaluation validation techniques and performance metrics.
Ability to translate research ideas into scalable production-level ML/AI solutions.
Excellent analytical communication and collaboration skills across multi-functional teams.
Hands-on experience in programming and implementing ML/AI models in Python or similar languages.
Experience in building and maintaining machine learning pipelines including data preprocessing model training and deployment.
Demonstrated ability to develop and apply statistical and machine learning models for prediction optimization or causal analysis.
Experience developing AI tools frameworks or APIs to support model deployment or LLM-based applications.

Experience with LLM fine-tuning prompt engineering or retrieval-augmented generation (RAG).
Familiarity with generative AI techniques (e.g. diffusion models transformer architectures).
Experience building scalable ML systems using cloud platforms (AWS GCP or Azure) and ML Ops tools (e.g. SageMaker Vertex AI MLflow).
Understanding of data engineering and feature pipeline automation.
Experience developing or contributing to AI frameworks APIs or internal tools used by other teams.
Strong software engineering practices including version control testing and code review.
Familiarity with cross-domain applications of AI/ML (e.g. marketing analytics personalization recommendation systems).

Key Skills

Laboratory Experience
Immunoassays
Machine Learning
Biochemistry
Assays
Research Experience
Spectroscopy
Research & Development
cGMP
Cell Culture
Molecular Biology
Data Analysis Skills

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