FAQs
What is the primary focus of the Machine Learning Engineer role at Tower Research Capital?
The primary focus of the Machine Learning Engineer role is to develop data pipelines, design and implement deep learning architectures, evaluate model performance, optimize existing models, and build pipelines for monitoring and improving production ML/DL models.
What qualifications are required to apply for this position?
Applicants should have a bachelor’s, master's, or PhD degree or equivalent from a top university, along with 2-6 years of experience in the field.
Is prior experience with deep learning models mandatory for this role?
Yes, prior experience with training, building, and deploying large scale deep learning models is mandatory.
What programming languages and tools should candidates be proficient in?
Candidates should be skilled in Python, and have hands-on experience with frameworks such as PyTorch or TensorFlow, as well as familiarity with Git, CI/CD pipelines, Linux, SQL, and BASH scripting.
What types of models should candidates have experience designing and implementing?
Candidates should have experience designing and implementing sequence and time series models.
What benefits does Tower Research Capital offer to its employees?
Benefits include a competitive salary and performance-based bonuses, 5 weeks of paid vacation per year, daily breakfast, lunch, dinner, and snacks, a cab facility within Gurgaon, and health club allowances.
Is this position suitable for fresh graduates?
No, this position requires 2-6 years of experience, so it is not suitable for fresh graduates.
What work environment does Tower Research Capital provide?
Tower Research Capital offers a cubicle-free workplace with a casual dress code and a focus on quality of life for employees.
Does Tower Research Capital hire people from diverse backgrounds?
Yes, Tower Research Capital is an equal opportunity employer and welcomes candidates from diverse backgrounds.
Are there opportunities for professional growth within the role?
Yes, the role includes opportunities for optimizing and fine-tuning existing models and contributing to the enhancement of in-house trading systems and strategies.