Responsible for building and deploying machine learning models in production environments, designing and implementing ML pipelines, and providing mentorship.
Responsibilities
Designing and developing machine learning (ML) models and algorithms
Implementing and maintaining ML pipelines and workflows
Collaborating with data scientists and analysts to deploy and monitor ML models
Developing and implementing ML deployment and monitoring strategies
Providing guidance and mentorship to junior ML engineers
Lead scientific innovation in AI-enabled biologics, antibody discovery, protein engineering, and protein design, translating complex drug‑discovery challenges into AI opportunities and partnering with cross‑functional teams to develop and evaluate AI methods for protein structure prediction and generative molecular design.
Responsibilities
Lead scientific innovation in AI-enabled biologics, antibody discovery, protein engineering, and protein design
Translate complex drug‑discovery challenges into well‑defined AI opportunities with clear hypotheses, evaluation methods, and success criteria
Develop and evaluate rigorous AI methods for protein structure prediction, biological foundation models, and generative molecular design
Assess emerging AI models, datasets, and technologies for scientific maturity, differentiation, limitations, and drug‑discovery value
Lead cross‑functional proof‑of‑concept studies connecting computational performance with experimental validation and meaningful scientific outcomes
Requirements
Doctoral degree in machine learning, computational biology, bioinformatics, structural biology, protein engineering, or a related discipline
Lead research on machine learning and large language models to advance AI capabilities for housing and healthcare applications.
Compensation
The salary range for this role is $150,000 - $230,000. EliseAI offers a competitive total rewards package which includes base salary, equity, and a comprehensive benefits & perks package.
Responsibilities
Develop and refine machine learning architectures optimized for advanced conversational flow
Conduct comprehensive analyses to support decision-making and improve the effectiveness of our AI solutions
Apply advanced mathematical and machine learning techniques to solve specific problems such as optimizing tenant engagement strategies, predicting tenant behavior, and improving customer interaction processes
Stay abreast of the latest research in machine learning and AI, and apply innovative approaches to improve our products
Work closely with cross-functional teams, including engineering, product development, and data science, to integrate and deploy machine learning models
As a Marketing Data Scientist, you will leverage our advanced data infrastructure on the Google Cloud Platform (GCP) to generate actionable insights.
Responsibilities
You conduct detailed analyses of customer, marketing, and onsite data using SQL and Python to optimize the operational management of marketing campaigns.
You will take end-to-end ownership of your analytical projects—from initial data analysis and preparation to model evaluation and the automated delivery of findings.
Expand and optimize Data Science use cases, such as marketing mix modelling or bidding optimizations, using open source technologies.
Develop and provide self-service BI solutions tailored specifically to marketing requirements and customer attributes.
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