About SupportLogic
SupportLogic SX™ is a platform that elevates customer service experience by leveraging natural language processing (NLP) and machine learning (ML). The platform seamlessly integrates with your existing ticketing system (such as Salesforce Service Cloud, Zendesk, Microsoft Dynamics), reads all the comments in every ticket to extract key signals related to customer sentiment and churn, predicting outcomes and providing proactive recommendations. Customer Support and Success organizations use the platform to stay on top of how their customers feel about them thus improving customer relationships, products and operations.
We are a well-funded startup with investments from top tier investors in Silicon Valley (Sorenson Ventures, Sierra Ventures) and a customer list that is a who’s-who of Enterprise IT companies. We are privileged to have customers who are not only outspoken fans of our product but also prove it by renewing every year.
How your work will support our growth:
The mission of the SupportLogic 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 our customers.
We are responsible for:
- Maximizing the value of SupportLogic to our customers by advancing the frontier of ML performance and Intellectual Property (IP).
- Ensuring ML models deliver consistent and accurate results, and working with backend engineers to improve performance in production environments.
- Extracting maximum utility from our predictions for our end users and customers by collaborating with product design, UI, and customer-facing teams.
We seek a Staff Machine Learning Engineer interested in improving any and all aspects of ML organization efficiency, with the ultimate goal of increasing customer confidence in our ML predictions and our ability to train new ML models or roll out new ML products quickly and efficiently. SupportLogic’s ML products are our core value proposition for our customers, so ML efficiency has a direct connection to our value to our 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 (such as LLMs and RAGs), systems, and processes.
The work you’ll do:
- Ship - Increase velocity of ML model deployment into production through automation of model management, deployment, and rollout processes.
- Validate - Increase confidence of model rollouts by enriching and automating model validation prior to and immediately after deployment.
- Measure - Provide insight into 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.
- 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.
- Build - Provide internal tools or incorporate commercial tools (e.g. Kubeflow, VertexAI, LangChain, LangSmith, etc) into data scientist workflows for data analysis, feature generation, model development, etc., to boost ML team productivity.
- 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.
About you (don't worry if you don't have this whole list- we expect you to learn with us):
- A self-starter, with the interest and passion to contribute in a fast-paced startup environment.
- Technical leadership experience, assisting in driving the ML team direction & vision
- B.S. degree or equivalent in Computer Science, Mathematics, or similar field of study.
- 5+ years of experience building ML products as a full-time machine learning engineer
- 3+ years of experience using Large Language Models in production
- Strong proficiency in software development and system design
- Fluent in Python
- Experience with common Python data science libraries such as PyTorch, HuggingFace, Pandas, numpy and scikit-learn
- Experience with the lifecycle of model training, evaluation and deployment
- Experience with using SQL
Preferences:
- Experience building APIs in Python, particularly in FastAPI or Flask.
- Experience with using Pytest, Docker, and sqlalchemy
- Experience with MLOps platforms such as KubeFlow or MLFlow
- Experience with modern data warehouse such as Snowflake, BigQuery or Hive
- Working experience at reputable companies such as FAANG
