Machine Learning Scientist
San Francisco, CA
Clara Labs is establishing a new class of assistant that understands you as a person would, but operates at the scale, speed, and persistence of a machine. To do this, we use a human-in-the-loop approach, mixing intelligent automation software with remote human workers to form a powerful and frictionless customer service.
Our initial focus is on scheduling meetings via email. We’ve observed the anxieties caused by cluttered inboxes, complex calendars, and juggling scheduling preferences. We’re out to eliminate that pain so our customers have more time to focus on what matters.
We aim to make automated assistants a truly everyday productivity tool. Our deep focus on conversation quality and flexibility via our human-in-the-loop approach differentiates us from other automated assistants and has helped us deliver a highly reliable and flexible natural language interface from day one.
You can read a little bit more about us here:
You will work on soup-to-nuts development of novel algorithms informed by our unique applications and constraints. Individuals in this role design data collection strategies, frame the right problems to solve, develop models, measure and compare model performance, and integrate these models into production features.
Read more about how our machine learning development works here:
- Be embedded with a product-focused engineering team to ensure...
- ML predictions are relevant and usable within the primary platform
- Confidence metrics can be integrated into automation systems
- Data/annotation collection facilities for each problem are baked into our platform
- Experience in one of the following and familiarity with several others:
- NLP / computational linguistics – techniques (e.g., pos tagging, dependency parsing, chunking, classification, ...) and tools (e.g., Stanford NLP, nltk, …)
- Bayesian inference – e.g., topic modeling, graphical generative models
- ML methods – e.g., svms, random forests, convex optimization, transfer learning
- Deep learning – rnn / cnn architectures, algorithms (e.g., rmsprop, adadelta, adam, ...), and tools (e.g., theano, torch, tensorflow, …)
- ML in practice – e.g., selection bias mitigation, ROC analysis, field performance analysis, data mining
- ML systems – event-driven real-time ML systems, pipelining and processing frameworks (e.g., Spark, Lucene, EMR)
- M.S. or Ph.D. in CS, EE, or equivalent
- Industry experience with ML systems is strongly preferred
Communicating machine learning results and capabilities are important on this team. Please include a cover letter explaining why you think you are a great match for Clara.
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.