Senior Machine Learning Engineer (Remote)

About Ensemble AI:

At Ensemble AI, we are pioneering a revolutionary approach to representation learning. Our innovative technology and ML algorithms redefine traditional methods, enabling ML practitioners to harness high-quality data representations effortlessly and across domains. Ensemble’s product Dark Matter drops into any existing ML pipeline and enhances data by approximating unobserved ‘missing’ variables without the need for any external data. Our technology is industry agnostic and can provide richer insights for any statistical model. Join us in transforming the landscape of data science and ML at Ensemble.

We are a Seed-stage company backed by Salesforce Ventures, M13, Motivate, and Amplo.

Job Overview:

We are looking for a Senior Machine Learning Engineer with a deep understanding of embeddings, particularly in tabular & time series models, and strong expertise in representation learning. You will be responsible for creating new product features based on client needs and expanding core algorithms in innovative ways. The ideal candidate has extensive industry experience, with a focus on designing, researching, and deploying machine learning models into production. This role will be highly collaborative, involving both frontier algorithmic research and practical deployment.

Key Responsibilities:

• Research, design, and implement machine learning models using state-of-the-art techniques.
• Work on embedding models, specifically tabular & time series embeddings, with a focus on representation learning in a new subset of the field. (experience in graphical models, vision and NLP in addition to these is a plus).
• Develop and influence the core algorithms, focusing on efficiency and innovation.
• Create new product features informed by client requirements, driving IP development and expanding existing algorithms.
• Collaborate with across teams and clients to integrate ML models into scalable production systems, ensuring efficient deployment and performance optimization.
• Work on cutting-edge ML methods and algorithms, driving innovation at the frontier of AI.
• Manage and scale models across GPU clusters and integrate with cloud platforms (e.g., AWS).
• Contribute to future work involving image and text data as needed, though prior computer vision or NLP experience is not required. (it’s a plus)

Requirements:

• Strong familiarity with embeddings, ideally in tabular / time series embedding models.
• Experience with ML research in an industry setting, focused on deploying production-ready models, evidenced by publications in top-tier conferences and journals.
• Expertise in representation learning and applying it to real-world problems.
• Advanced Degree in Computer Science, Engineering, Mathematics, Physics or a related field, with a focus on machine learning research.
• 5+ years of hands-on experience in machine learning, with a demonstrated track record of designing and deploying ML models in production environments.
• Strong proficiency in modern programming languages (such as Python) and deep familiarity with leading machine learning libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Extensive experience with AWS and proficiency in Python.

Preferred Qualifications:

• Experience working on cutting-edge algorithms and pushing them to production.
• Startup experience and a passion for driving innovation in a fast-paced environment.
• Experience scaling ML models in production environments and across GPUs.
• Hands-on experience with MLOps tooling and product integrations.
• Collaborative mindset, particularly in a research-to-production workflow.