Soroco is on a mission to elevate and transform how teams everywhere work.
Powered by multiple patents, its flagship AI model, Scout, generates a work graph - a map of friction hurting your teams and business outcomes.
Today, this map drives productivity improvements in 150+ organizations globally, including several Fortune 500 companies.
Scout and the work graph have been featured in Forbes, Fortune, Harvard Business Review, and was listed on Bloomberg’s list of ideas that defined 2022.
With operations spanning Boston, London, and Bangalore, Soroco was founded by alumni of Harvard, MIT, and Carnegie Mellon.
Visit www.soroco.com to learn how we help teams discover their work graph.
What we are looking for: -
The ideal SE for this role is passionate about building machine learning models that are fine-tuned to perform powerful model tasks such as classification, summarization, question answering, and generation. An ability to take direction on the model design and how we should evaluate the models will be important. Some fundamentals of machine learning model training and fine-tuning is required, but some growth and learning is expected for someone in this role who will be mentored by PhDs in this field. Being capable of working with large data sets and performing basic transformations on them for training and developing the models will also be important.
The role purpose and scope: -
The Software Engineer (SE) will drive the company’s technical growth and delivery by working with engineering teams. An SE will typically work in, and mentor, product development to create and deliver complex proprietary systems.
- Design, architect, and build high-quality scalable systems.
- Own projects end-to-end, including gathering requirements, solutioning and designing architecture, developing, testing, deploying, and maintaining systems.
- Interact and collaborate with our high-quality technical team across India and the US
Experience and skills:-
- 1-3 Years of work experience designing, training and implementing machine learning models.
- Majority of experience in the field may come from educational course work.
- Experience with natural language processing (NLP) and machine learning techniques.
- Strong fundamental understanding of deep learning models.
- The ability to design experiments, evaluate a model's performance comprehensively, and debug issues during model training.
- Experience with building datasets.
- Experience with training and fine-tuning NLP models (specifically Transformer-based models) like fastText, BERT, T5, etc.
- Working on constrained computational resources and optimizing models for efficiency, and cost-effectiveness.
- Knowledge of data preprocessing, feature engineering, and data augmentation techniques.
- Python, Scikit-learn, TensorFlow or PyTorch.
Bonus factors: -
- Ability to learn and grasp any problem quickly.
- Able to fine-tune models or experiment with them at a rapid pace.
- Ability to follow potential model designs and prototype or fine-tune them.
- Desire to design and build large, enterprise-grade software systems from scratch.
