Software Engineer (NLP/Deep Learning) - Cresta - Palo Alto

Role Description


Cresta's machine learning team develops cutting edge solutions to empower our suite of enterprise products. We use a full range of machine learning technologies from probabilistic models to deep learning. Cresta is looking for talented software engineers with an academic or industrial background in machine learning.


Company Description


Cresta is an early-stage enterprise AI startup with the mission of ending repetitive work, and transforming all sales agents into experts. We enable large human workforces to operate like autonomous, intelligent fleets - when one human makes a mistake, everyone learns from it, and when one human achieves success, everyone improves.


Spun out of the Stanford AI lab and chaired by Google-X founder Sebastian Thrun, our team is composed of Stanford PhDs and top engineers and leaders from Google, Facebook, and other tech companies. Even as an early-stage startup, multiple Fortune 500 companies see the value of Cresta to enhance their workforce productivity with AI.



    • Design and develop infrastructure to collect high-quality labels for large-scale datasets.
    • Research and implement cutting edge deep learning architecture to solve open-ended problems.
    • Work with product team to code, train and deploy production-grade machine learning systems.
    • Help shape the direction of machine learning and artificial intelligence at Cresta.


    • BS, MS or PhD in Computer Science, Machine Learning, NLP or a related technical field.
    • Strong software engineering skills across multiple languages including but not limited to Python, Javascript, etc.
    • Experience building one of the following production systems in industry: natural language processing, bayesian models, information retrieval, learning to rank, recommender systems, speech recognition, unsupervised learning.
    • Good mathematical understanding of popular NLP and Machine Learning algorithms.
    • Hands-on experience with NLP tools, libraries and corpora (e.g. NLTK, Stanford CoreNLP, Wikipedia corpus, etc).


    • React
    • GraphQL
    • Django
    • Postgres
    • Tensorflow and PyTorch
    • Kubernetes
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