About Frame AI
Frame allows companies to surface and act on voice-of-customer insights in real-time, essentially acting as a dedicated data team delivered as a SaaS product. Frame actively listens across channels and acts as an early warning system that delivers prioritized customers, themes, and team behaviors that need attention, to increase the quality of the customer experience and avoid churn.
We help customers be heard.
Frame has some great advantages for an early-stage startup:
Our founding team has deep experience, education, and network and is intensely collaborative
We are backed by some of the East Coast’s top VCs (FirstMark, Greycroft, G20, and others)
We have prestigious customers of diverse size and industry paired with sustained growth
We are working on some of the most exciting technical challenges of our day, accelerated by recent advances in natural language technology research and deep learning
We offer an opportunity to have major impact on challenging problems, surrounded by supportive coworkers with competitive pay and great benefits including a generous family leave policy
Data is at the heart of everything Frame does. Our Data Team excels at turning cutting edge A.I. research into scalable, value-generating data products. We’re obsessed with both quality and time-to-value, enabling our success team to deliver impressive results to our customers the day they sign up. Data at Frame means thinking about problems scientifically from first principles, then assembling or developing the right tools for the task. While python is the glue that holds our systems together, we’re not tool, library or method partisans – and you shouldn’t be either! Importantly, our data products are designed to work on behalf of the populations they analyze, always with an eye toward empowering agency, not replacing it.
Frame is a professional and intellectual community, with supportive and talented people who are proud of what they build. Our collaborative environment is an ideal place to deliver to your potential while bringing more attention, listening, and understanding to the conversations people have with the companies serving their everyday needs.
A critical part of the value Frame delivers is in navigating and annotating the semantics expressed in conversational data. To deliver that value we have developed a suite of models and tooling that automatically annotate conversations based on their semantic content and easily allows subject matter experts to train novel domain-specific models with minimal effort. As the owner of Semantic Applications you will be responsible for the accuracy, ease of use, and scalability of these models and tools. Every Frame customer has slightly different data streams and different need for analysis, which is why we provide exploratory tools for both customers and our success team to fine tune Frame’s analytics and engine to suit. Your responsibility will be to make these tools ever more accurate and efficient to use – ultimately helping to scale our onboarding process and making sure that Frame is growing efficiently and getting more effective with every customer. This is a critical and highly collaborative role. Successful candidates will utilize a wide variety of ML methods and tooling to empower internal and external non-technical users to navigate the semantics of their conversations.
Strong machine learning fundamentals including MLE, sampling techniques, rare event detection, high dimensional feature spaces, feature engineering, regularization, dimensionality reduction, unsupervised clustering, transfer learning, zero-shot learning
Significant experience with NLP methods and problem spaces, including both traditional linguistic methods and more recent statistical DNN methods
Significant experience with rigorous scientific evaluation, hypothesis testing, and experimental design
Significant ETL expertise (preferably SQL) including schema design, query optimization, and data migration techniques
Demonstrated ability to identify and communicate effectively on the product and business implications of modeling and implementation decisions, including collaboration with non-technical team-members
Intermediate or greater Python language ability and past or current expert-level knowledge of at least 2 industry-relevant programming languages including SQL
Proven track record as a lead maintainer of inferential systems deployed in a complex technical environment for at least 6 months consecutively
Demonstrated ability to evaluate emergent technologies such and bring them successfully into prototypes
Demonstrated ability to balance challenging constraints in go-to-market speed, product capability, available resources, and impacts on a roadmap
5+ years experience building human facing data products
Production experience with deep neural net frameworks (pytorch, tensorflow) and different neural architectures (transformers, LSTM, RNN, etc.)
Working knowledge of statistical language models including fine tuning, overcoming catastrophic forgetting