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About Demandbase: Demandbase helps B2B companies hit their revenue goals using fewer resources. How? By using the power of AI to identify and engage the accounts and buying groups most likely to purchase. Our account-based technology unites sales and marketing teams around insights that you can understand and facilitates quick actions across systems and channels to deliver big wins. As a company, we’re as committed to growing careers as we are to building world-class technology. We invest heavily in people, our culture, and the community around us. We have offices in the San Francisco Bay Area, New York, Seattle, and teams in the UK and India. We are Great Place to Work Certified. We're committed to attracting, developing, retaining, and promoting a diverse workforce. By ensuring that every Demandbase employee is able to bring a diversity of talents to work, we're increasingly capable of achieving our mission to transform the way B2B companies go to market. We encourage people from historically underrepresented backgrounds and all walks of life to apply. Come grow with us at Demandbase! About the Role: As a Senior ML Engineer, you’ll have a strategic role in driving data-driven insights and developing production-level machine learning models to solve high-impact, complex business problems. This role is suited for an individual with a strong foundation in both deep learning and traditional machine learning techniques, capable of handling challenges at scale. You will work across teams to create, optimize, and deploy advanced ML models, combining both modern approaches (like deep neural networks and large language models) and proven algorithms to deliver transformative solutions. Responsibilities: 1.Machine Learning Model Development and Productionization Develop, implement, and productionize scalable ML models to address complex business issues, optimizing for both performance and efficiency. Create and refine models using deep learning architectures as well as traditional ML techniques. Collaborate with ML engineers and data engineers to deploy models at scale in production environments, ensuring model performance remains robust over time. 2.End-to-End Solution Ownership Translate high-level business challenges into data science problems, developing solutions that are both technically sound and aligned with strategic goals. Own the full model lifecycle, from data exploration and feature engineering through to model deployment, monitoring, and continuous improvement. Collaborate with cross-functional teams (product, engineering, analytics & research) to embed data-driven insights into business decisions and product development. End-to-end ownership and resilience in production environment 3.Experimentation, Testing, and Performance Optimization Conduct rigorous A/B tests, evaluate model performance, and iterate on solutions based on feedback and performance metrics. Employ best practices in machine learning experimentation, validation, and hyperparameter tuning to ensure models achieve optimal accuracy and efficiency. 4.Data Management and Quality Assurance Work closely with data engineering teams to ensure high-quality data pipeline design, data integrity, and data processing standards. Actively contribute to data governance initiatives to maintain robust data standards and ensure compliance with best practices in data privacy and ethics. 5.Innovation and Research Stay at the forefront of machine learning research and innovations, particularly in neural networks, generative AI, and LLMs, bringing insights to the team for potential integration. Prototype and experiment with new ML techniques and architectures to improve the capabilities of our data science/ML solutions. Support AI-strategy for Demandbase and align business metrics with data science goals. 6.Mentorship and Team Leadership Mentor junior data scientists/ML Engineers and collaborate with peers, fostering a culture of continuous learning, innovation, and excellence. Lead technical discussions, provide guidance on best practices, and contribute to a collaborative and high-performing team environment. Required Qualifications: Education : B.tech/M.Tech in Computer Science or Data Science. Bachelor’s degree in computer science, statistics, maths, or science. Master’s degree in data science, computer science, or a related field Experience : 8+ years of experience in data science/ML, with a strong emphasis on production-level ML models, including both deep learning and traditional approaches. Technical Skills : Expertise in deep learning frameworks such as TensorFlow, PyTorch, or Keras. Proficiency in Python and experience with data science libraries (e.g., scikit-learn, Pandas, NumPy). Strong grasp of algorithms for both deep neural networks and classical ML (e.g., regression, clustering, SVMs, ensemble models). Experience deploying models in production, using tools like Docker, Kubernetes, and cloud platforms (AWS, GCP). Knowledge of A/B testing, model evaluation metrics, and experimentation best practices. Proficient in SQL and experience with data warehousing solutions. Familiarity with distributed computing frameworks (Spark, Dask) for large-scale data processing. Soft Skills : Exceptional problem-solving skills with a business-driven approach. Strong communication skills to articulate complex ideas and solutions to non-technical stakeholders. Ability to lead projects and mentor team members. Good to have skills Experience with LLMs, transfer learning, or multimodal models for solving advanced business cases. Experience with tools and models such as LLAMA, high-volume recommendation systems, duplicate detection using ML. Understanding of ML Ops practices and tools (e.g., MLflow, Airflow) for streamlined model deployment and monitoring. Experience in data observability, CI/CD What We Offer Opportunity to work in a cutting-edge environment, solving real-world business problems at scale. Competitive compensation and benefits, including health, wellness, and educational allowances. Professional growth opportunities and support for continuous learning. This role is ideal for a data science/ML Engineer who is passionate about applying advanced machine learning and AI to drive business value in a fast-paced, high-impact environment. If you’re eager to innovate and push boundaries in a collaborative and forward-thinking team, we’d love to meet you! Show more Show less
