Benefits:

• competitive salary

• stock options

• personal development and learning programs

Education Requirements:

• PhD degree

• Masters degree

Experience Requirements:

• 4+ years of experience of applied research in Machine Learning

Other Requirements:

• Comprehensive and deep knowledge in fields such as machine learning/statistical modelling, deep learning, NLP, NLU, graph learning or reinforcement learning/user intent modelling with at least one publication as the first author in a leading conference or journal related to one of these fields

• Computer Science fundamentals in algorithm design, complexity analysis, data structures, problem-solving and diagnosis

• Experience applying machine learning research in information retrieval, search, information discovery or recommender systems

• Solid experience with programming languages such as Python

• Good written and spoken English communication skills and the ability to present complex solutions with clarity

• Flexible and responsive to spontaneous needs, challenges and opportunities, able to balance conflicting demands on time and priorities

• Highly self-organized, planned and collaborative with a strong personal drive to keep up with a productive mindset

• Continual passion to learn and apply knowledge in a methodical fashion

Responsibilities:

• Develop machine learning models to build our peer-group analysis technology for finance professionals

• Drive the Comparables.ai ML technology in close collaboration with other machine learning scientists, software engineers and product managers

• Initiate and define new research and development ideas to solve the peer-group analysis problem, drive execution, and align across the teams and collaborators

• Work with the tech stack of Python/Tensorflow/Keras/Pytorch/Jupyter notebooks

• Write reusable, testable, and efficient code

Benefits:

• competitive salary

• stock options

• personal development and learning programs

Education Requirements:

• Master or PhD in Computer Science

Experience Requirements:

• Extensive background in AWS Cloud Architecture and DevOps CI/CD practices

Other Requirements:

• Profound expertise in MLOps (collect, store, manage data and models), creating training datasets (data labelling, feature engineering, data partitioning, sampling and slicing), building and training machine learning infrastructure, model deployment in production (inference constraints, model compression, evaluations)

• Knowledge of ML infrastructure monitoring and maintenance

• Familiarity with architectural choices for ML systems

• Prior experience working in collaboration with Machine Learning scientists

• Hands-on experience implementing machine learning systems at scale in Python or similar languages and a variety of libraries like Tensorflow/Keras/Pytorch/Numpy/Pandas/Scikit-learn

• Understanding of fundamental design principles behind a scalable AI and data-driven applications

• Solid experience of working with well-known cloud computing and web services such as AWS

• Good coding skills and engineering practices with agile software process and research-driven development

• Knowledge of DevOps methodologies and project management tools such as Git, Jira, Confluence..,

• Good written and spoken English communication skills and be able to present complex solutions with clarity

• Flexible and responsive to spontaneous needs, challenges and opportunities, able to balance conflicting demands on time and priorities

• Highly self-organized, planned and collaborative with a strong personal drive to keep up with a productive mindset

• Continual passion to learn and apply knowledge in a methodical fashion

Responsibilities:

• Build research and development pipelines for machine learning, deep learning, NLP, NLU, graph learning or reinforcement learning/user intent modelling systems

• Collaborate closely with in-house scientists on Machine Learning Operations MLOps and tasks ranging from data management to training and deployment of models

• Develop data collections, training and evaluation pipelines for research and development of machine learning models

• Support deployment of models in collaboration with software engineers to integrate successful research results into the production systems

• Initiate and define new MLOps ideas to productize the peer-group analysis technology efficiently and drive its execution

Benefits:

• competitive salary

• stock options

• personal development and learning programs

Education Requirements:

• Extensive background in AWS Cloud Architecture and DevOps CI/CD practices

Experience Requirements:

• Extensive background in AWS Cloud Architecture and DevOps CI/CD practices

Other Requirements:

• Profound expertise in cloud computing concepts and design considerations

• Extensive background in AWS Cloud Architecture and DevOps CI/CD practices

• Solid working experience with AWS services such as EC2, VPC, ELB, S3, RDS, Lambda, DynamoDB, Kubernetes, CloudWatch, Elastic

• Knowledge of Cloud Networking including firewalls and Security

• Demonstrable experience in leading AWS focused cloud programs

• Able to work with developers/engineers directly in a 24/7 production environment

• Experience with performance testing, tuning and optimization

• Understanding of fundamental design principles behind scalable AI and data-driven applications

• Good knowledge of coding and engineering practices with agile software processes and research-driven development

• Knowledge of project management tools such as Git, Jira, Confluence, etc.

• Good written and spoken English communication skills and being able to present complex solutions with clarity

• Flexible and responsive to spontaneous needs, challenges and opportunities, able to balance conflicting demands on time and priorities

• Highly self-organized, planned and collaborative with a strong personal drive to keep up with a productive mindset

• Continual passion to learn and apply knowledge in a methodical fashion

Responsibilities:

• Implementing innovative cloud computing solutions and solving the most complex technical problems underlying our AI-based SaaS product in cost-effective ways

• Working with machine learning scientists/engineers and software developers on defining global best practice standards for cloud security, automation, redundancy and scalability

• Designing, testing and implementing new cloud-based solutions using these best practices

• Influencing the direction of projects, advising on cloud solution options and preparing indicative cost estimates

• Auditing existing solutions to identify optimizations and bring them in line with standards