Software Engineer - Machine Learning

Did you know that Money Laundering is a primary enabler of criminal activity like drug trafficking, smuggling, terrorism, and corruption around the world with an estimated $2.3 Trillion laundered annually? Criminals are becoming more and more sophisticated in rapidly innovating new ways to launder money and current methods of detecting money laundering are antiquated and ineffective. Quantiply’s Sensemaker application suite and platform solutions use AI and machine learning algorithms to identify money laundering and other criminal activities and automatically recommend mitigation strategies and actions. If you are looking for an opportunity to work on complex business problems using the latest AI and Machine Learning technologies, work with best and brightest in crafting innovative solutions while making a positive impact on society, Quantiply is the place for you.

We’re looking for software engineers with experience in machine learning and artificial intelligence. You will be embedded as part of a team that collaborates with researchers on conceiving, researching, and prototyping new machine learning techniques and use cases with the goal of driving Quantiply’s growth in the Anti-Money Laundering space.

Ideal candidates will have a good understanding of state-of-the-art techniques in machine learning and deep learning, performance optimization, and benchmarking, along with a strong understanding of high-performance computer architecture. Candidates must also possess strong verbal and written communication skills and the demonstrated ability to work in a demanding team-oriented environment.

Responsibilities:

• Develop highly scalable deep learning, reinforcement learning and bayesian models.
• Support research projects by providing innovative designs for end-to-end Machine Learning systems.
• Optimize performance of complex machine learning systems. Exploit modern parallel environments.
• Design and develop software libraries.
• Partner with Product and Engineering teams to explore new opportunities.
• Influence product features and product roadmap through exploratory analysis.
• Report and present software developments verbally and in writing.

Minimum qualifications:

• PhD in computer science, machine learning, electrical engineering, mathematics, or equivalent pracitical experience.
• Strong knowledge and experience in Python, Docker and Kubernetes.
• Working experience in Tensorflow.
• Working experience with distributed software architecture.
• Knowledge of machine learning and statistics.

Preferred qualifications:

• Strong experience in Tensorflow or similar frameworks.
• Strong experience with concurrent and distributed software architecture.
• Experience with Hadoop.
• Experience with C++/Java/Scala.

All your information will be kept confidential according to EEO guidelines.