Senior Machine Learning Engineer (Python)

‍Join the team remotely for San Francisco-based company from Mexico!An exclusive opportunity to work directly with a cutting-edge data service technology company, directly with the SourceScrub team.

‍What is SourceScrub?

‍SourceScrub is the world's leading data service for firms looking to research, find and connect with privately held companies. Our Private Company Intelligence platform allows deal teams to take a data-driven approach in a traditionally opaque segment of the market. We combine state-of-the-art technology with an unmatched QA process resulting in the freshest and most accurate data set available.SourceScrub provides research and information management systems that incorporate thousands of online sources from trade show exhibitor lists to industry buyers' guides. SourceScrub is working 24/7 to ensure we offer accurate and current data for prospecting investment opportunities. Our sourcing data and prospecting tools promise to save time and increase deal flow.

Responsibilities

• Implementing different ML solutions in the field of NLP such as recommendation engines or semantic similarity
• Research and evaluate state-of-the-art methods and tools for various problems
• Supporting and advising the team in implementing and adopting different ML models and solutions
• Working in the client-facing team and P2P communication with the stakeholders

Experience/Qualifications

• 5+ years of commercial experience with ML and Data Science.
• Strong expertise in NLP and its core concepts is a must.
• Experience working with LLMs and transformers (e.g. BERT, Longformer, GPT-3 family).
• Key skills: Python, application development, production deployments, working with ML technologies, working with ML models.
• Knowledge and practical experience with cloud technologies such as Azure, GCP, or AWS (with preference for Azure).
• Experience working in cloud-based ML platforms such as Databricks.
• Сomplete independence and results orientation.
• Upper-Intermediate or higher level of English.

Nice to have:

• Previous work on recommendation systems and/or embeddings for semantic searches.
• Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests, and proper usage, etc.) and experience with applications of statistical methods.
• Knowledge of spaCy and similar NLP libraries like NLTK, textacy etc.
• Any experience or interest in creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc.

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