ML Engineer - Demand Geneneration / Research / Engineering

• We are looking for an ML Engineer – Demand Generation / Research / Engineering to build intelligent systems that leverage machine learning, data science, and AI to drive business growth and demand generation.
• You will work on translating business challenges into scalable ML solutions, combining experimentation, research, and engineering to build systems that deliver measurable business impact.
• The ideal candidate is a hands-on ML engineer with strong technical depth, a research-oriented mindset, and the ability to apply ML beyond traditional use cases.
• Research, design, and develop ML models and intelligent systems for demand generation, lead scoring, conversion prediction, and growth optimization.
• Build and deploy scalable ML pipelines for data processing, feature engineering, model training, evaluation, and inference.
• Explore and implement ML techniques for audience segmentation, behavioral modeling, personalization, and predictive analytics.
• Experiment with LLMs, generative AI, recommendation systems, and advanced ML techniques to develop intelligent business applications.
• Translate research ideas and business requirements into production-ready ML solutions.
• Design data pipelines and experimentation frameworks to support targeting, campaign optimization, and performance measurement.
• Collaborate with cross-functional teams across Engineering, Research, Product, and GTM to identify opportunities for AI-driven automation and optimization.
• Evaluate emerging ML techniques, conduct experiments, and continuously improve model performance, scalability, and reliability.
• Establish robust evaluation frameworks to measure model effectiveness and business impact.
• 3–7 years of experience in Machine Learning Engineering, Applied Research, Data Science, or related technical roles.
• Strong programming skills in Python and experience with ML frameworks such as PyTorch or TensorFlow.
• Strong understanding of machine learning fundamentals, statistical modeling, optimization, and model evaluation.
• Hands-on experience building ML pipelines, deploying models, and working with large-scale datasets.
• Experience applying ML to real-world problems such as predictive modeling, recommendation systems, personalization, user modeling, or intelligent automation.
• Ability to independently explore research problems, design experiments, and translate findings into scalable engineering solutions.
• Strong problem-solving skills with the ability to work across research, engineering, and business-oriented challenges.
• A builder's mindset with strong ownership, curiosity, and a willingness to experiment and iterate quickly.

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