Requirements
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B.S. degree or equivalent in Computer Science, Mathematics, or a similar field of study.
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Professional experience as a full-time machine learning engineer.
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5+ years of experience building ML products
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2+ years of experience using Large Language Models in production
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Strong proficiency in software development and system design
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Fluent in Python
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Understanding and use of common Python data science libraries such as PyTorch, HuggingFace, Pandas, NumPy and scikit-learn,
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Experienced with the lifecycle of model training, evaluation, and deployment
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Experienced with using SQL and modern data warehouses such as Snowflake
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Experienced building APIs in Python, particularly in FastAPI or Flask.
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Experienced with using Pytest, Docker, and sqlalchemy
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Experienced with MLOps platforms such as KubeFlow or MLFlow.
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A self-starter, with the interest and passion to contribute in a fast-paced startup environment.
What you’ll be working on
Our client is a well-funded and highly disruptive SaaS platform company that enables innovative companies like Salesforce, Nutanix, HPE Aruba Networks, Qlik, and Databricks to transform their Voice of the Customer (VoC) programs and harness true customer sentiment signals in real-time to proactively improve customer relationships, products, and operations while decreasing churn and top-line revenue leakage. They hire big-picture thinkers who can simultaneously roll up their sleeves and deliver with measurable impact. They dream in years, plan in months, and execute in days. Their culture is honest, fast-paced, collaborative, and very down-to-earth.
The mission of the Machine Learning (ML) team is to create and leverage cutting-edge ML models, especially Large Language Models (LLMs) that can extract new signals from unstructured data and make insightful, actionable predictions for customers.
They are responsible for:
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Maximizing the value of the company to their customers by advancing the frontier of ML performance and Intellectual Property (IP).
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Ensuring ML models deliver consistent, predictable, and improving performance in production environments by working with backend engineering.
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Extracting maximum utility from their predictions for end users and customers by collaborating with product design, UI, and customer-facing teams.
They seek a Machine Learning Engineer interested in improving any and all aspects of ML organization efficiency, with the ultimate goal of increasing customer confidence in their ML predictions and their ability to train new ML models or roll out new ML products quickly and efficiently. Our client’s machine learning products are their core value proposition for their customers, so ML efficiency has a direct connection to their value to customers and as a company.
You will be working in a fast-moving and growing company; applicants should be self-starting and comfortable learning and using new technologies, systems, and processes.
The work you’ll do:
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Ship - Increase the velocity of ML model deployment into production through automation of model management, deployment, and rollout processes.
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Validate - Increase confidence in model rollouts by enriching and automating model validation prior to and immediately after deployment.
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Measure - Provide insight into the accuracy and relevance of ML model predictions in production by measuring and monitoring model input and output data distributions, as well as user engagement/feedback on predictions.
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Automate - Incorporate user feedback/activity into new ML model training by automation of data collection, model retraining, model measurement, etc., towards a goal of continuous automated model retraining.
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Build - Provide internal tools or incorporate commercial tools (e.g., Kubeflow) into data scientist workflows for data analysis, feature generation, model development, etc., to boost ML team productivity.
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Collaborate - Bridge the gap between ML research and production-grade backend code by working with other engineering teams to integrate new ML models or APIs into production.
