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.

This role will need to overlap 3-5 hours/day with Pacific Standard Time.

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, predictable, and improving performance in production environments by working with backend engineering.
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 Machine Learning Engineer interested in building models our customers can rely on for accurate predictions and reading of signals. You will be working in a fast-moving and growing company; our team is made of self starters who are curious about learning and using new technologies, systems, and processes.

The work you’ll do:

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., ClearML) 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):

Professional experience as a data scientist, machine learning researcher, or machine learning engineer researching and/or building ML products
Strong proficiency in software development and systems design, ML model management, deployment, and/or versioning.
Fluent in Python and significant use of common Python data science libraries such as Pandas, numpy, and scikit-learn.
Experienced with querying and using SQL.
Experienced building APIs in Python, particularly in FastAPI or Flask.
A self-starter, with the interest and passion to contribute in a fast-paced startup environment.
B.S., degree or equivalent in Computer Science, Mathematics, or similar field of study with special focus on machine learning/NLP. M.S or M.Tech is highly desirable.

Preferences:

Experienced with common Python deep learning libraries such as PyTorch and TensorFlow.
Experienced with cloud platforms (AWS, GCP, Azure).
Experienced with MLops platforms such as KubeFlow or MLFlow.
Understanding and use of Pytest, Docker, and sqlalchemy.

Anticipated Comp Rate: $20-$50k USD/YR Based on experience.

How we support our employees' growth and well-being (an abbreviated list- our goal is to have healthy, well-rounded employees):

Healthcare (medical, dental, and vision) for employee+ family is fully covered by us + FSA, Teledoc, EAP program, and more- your health is our top priority, otherwise, you can't be expected to be your best at work.
ClassPass discount- whether it's yoga, bootcamp, cycling, or meditation that you crave, we're here to help make fitness accessible.
401k match- we match the first 3.5% of your contributions
Remote 1st, collaborative, and transparent culture.
Unlimited PTO where you're encouraged to take to recharge.

Our differences make us better:

Our commitment to diversity and inclusion is deep and core to the company we're building. Our users are diverse, and we believe that our company needs to reflect the diversity of our users in order to build the best possible product. Discrimination of any type will not be tolerated at SupportLogic, and we pride ourselves in making sure that all employees feel heard, supported, and challenged. To that end, SupportLogic prohibits discrimination on the basis of race, color, religion, creed, sex, age, marital status, national origin, mental or physical disability, political belief or affiliation, veteran status, sexual orientation, gender identity and expression, genetic information, and any other class of individuals protected from discrimination under state or federal law in any aspect of employment and application for employment.

If you are a California resident, please see our CCPA job applicant notice:

View our CCPA Job Applicant Notice

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1400 Fashion Island Blvd. Ste. 1010
San Mateo, CA 94404
Tel: 650.854.1000
info@sierraventures.com

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