Our client, an emerging start-up, is building a revolutionary recommender system, utilizing Artificial intelligence, for the use of their Enterprise Clients. With this platform, hundreds of personalized campaigns can be generated and deployed in minutes, moving beyond static, rules-based segmentation and targeting. The platform is user-friendly, integrates seamlessly with major data and customer engagement tools, and optimizes internal resources. Founded by experts from Google, Meta, and Lyft, their team is driven by a vision to shift from static segmentation to real-time, ML-driven decision-making, delivering significant revenue improvements and cost reductions.
Requirements
- Proficiency in ML frameworks (TensorFlow, PyTorch, Jax) and the Python data science ecosystem (Numpy, SciPy, Pandas, etc.).
- Experience with LLMs, RAG, and information retrieval in building production-grade solutions.
- Expertise in recommender systems, feed ranking, and optimization.
- Knowledge of causal inference techniques and experimental design.
- Experience with cloud services for managing and consuming large-scale datasets.
Responsibilities
- Designing, implementing, and researching machine learning algorithms to address growth and personalization challenges, with continuous fine-tuning and improvement.
- Developing AI-driven autonomous agents to execute complex workflows across diverse applications and objectives.
- Implementing and enhancing offline model evaluation methods.
- Analyzing large business datasets to extract insights and answer critical business questions using statistical or machine learning methodologies.
- Communicating data-driven insights and recommendations to product, growth, and data science teams at the client’s customers.
- Staying current on ML research through literature reviews, conferences, and networking.
Other
- Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
- 7+ years of experience training, improving, and deploying ML models and developing production software systems (e.g., data pipelines, dashboards) at fast-growing technology companies.
- Candidates must be authorized to work in the United States
- Thrives in ambiguity, with an ownership mindset and willingness to contribute beyond the role (e.g., product vision, strategic roadmap, engineering).
