Principal Full-Stack Data Scientist - Foundational Models
New
J
JobgetherMachine learning
Opportunity to work remotely within the United States.Full-TimePrincipal
Salary$200,000–$237,000 USD
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Job Details
- Experience
- At least 8 years of experience designing, developing, and deploying machine learning solutions
- Required Skills
- PythonSQLMachine LearningPyTorchSparkTensorflowA/B testingLLM
Requirements
- Bachelor's degree in Computer Science, Statistics, Mathematics, Physics, or another quantitative discipline; a Master's degree or PhD is preferred.
- At least 8 years of experience designing, developing, and deploying machine learning solutions, ideally in personalization, recommendation systems, search, or representation learning.
- Strong Python programming skills, including the ability to architect technical solutions and develop maintainable, production-grade code.
- Ability to lead ambiguous machine learning problems from exploration and prototyping through deployment, monitoring, iteration, and measurable business impact.
- Experience working with large-scale datasets using SQL and distributed data-processing technologies such as Apache Spark.
- Hands-on experience with modern deep learning frameworks, including PyTorch or TensorFlow.
- Strong understanding of model evaluation and experimentation, including offline metrics, A/B testing, and statistical assessment.
- Ability to assess production ML trade-offs involving predictive quality, scalability, latency, reliability, infrastructure requirements, and computational costs.
- Experience developing or applying deep learning, embeddings, representation learning, recommendation algorithms, LLMs, or multimodal models.
- Technical leadership and problem-solving skills, including establishing architecture, influencing technical decisions, and driving projects forward.
Responsibilities
- Design, develop, evaluate, and deploy production-ready machine learning models for personalization, recommendations, and search.
- Advance foundational modeling capabilities, including client and item representations, embeddings, retrieval and ranking systems, recommendation algorithms, and assortment generation.
- Explore and implement deep learning, representation learning, large language models, multimodal modeling, and generative AI approaches.
- Own the machine learning lifecycle from problem definition and data exploration through prototyping, experimentation, deployment, monitoring, and improvement.
- Develop offline evaluations and online experiments, including A/B tests, to assess model performance and quantify impact.
- Analyze and process large-scale behavioral and product datasets using Python, SQL, and distributed computing technologies.
- Build scalable, production-quality ML systems while managing model quality, latency, computational cost, observability, and operational resilience.
- Collaborate with Product, Engineering, Data Science, and other technical teams to deliver reusable foundational models and capabilities.
- Contribute to technical direction through architecture discussions, research, prototyping, code reviews, and modeling and engineering best practices.
- Mentor Data Scientists and engineers and communicate modeling approaches, trade-offs, findings, and recommendations to stakeholders.
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