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We're looking for a Machine Learning Engineer who is good at understanding and working with images. Your main job will be to create a system that can look at pictures of games and figure out what different parts do and how they work.

Responsibilities:

• Build a machine learning system that can look at game screen images and identify different parts.
• Make sure the system can guess what each part does when someone uses it.
• Work with other team members to make sure the system works well in our products.
• Keep checking and improving the system to make it better.
• Explain how you built the system and how it works to other team members.

Experience:

• It’s great if you’ve built systems like this before.
• If you have any projects or research in machine learning or image processing, we’d love to hear about it.
• Experience with cloud services (like AWS or Google Cloud) for running big projects is helpful.

Data driven decision-making is core to advertising technology at AdElement. We are looking for sharp, disciplined, and highly quantitative machine learning/ artificial intellignce engineers with big data experience and a passion for digital marketing to help drive informed decision-making. You will work with top-talent and cutting edge technology and have a unique opportunity to turn your insights into products influencing billions. The potential candidate will have an extensive background in distributed training frameworks, will have experience to deploy related machine learning models end to end, and will have some experience in data-driven decision making of machine learning infrastructure enhancement. This is your chance to leave your legacy and be part of a highly successful and growing company.

Required Skills

- 3+ years of industry experience with Java/ Python in a programming intensive role

- 3+ years of experience with one or more of the following machine learning topics: classification, clustering, optimization, recommendation system, graph mining, deep learning

- 3+ years of industry experience with distributed computing frameworks such as Hadoop/Spark, Kubernetes ecosystem, etc

- 3+ years of industry experience with popular deep learning frameworks such as Spark MLlib, Keras, Tensorflow, PyTorch, etc

- 3+ years of industry experience with major cloud computing services

- An effective communicator with the ability to explain technical concepts to a non-technical audience

- (Preferred) Prior experience with ads product development (e.g., DSP/ad-exchange/SSP)

- Able to lead a small team of AI/ML Engineers to achieve business objectives

Responsibilities

- Collaborate across multiple teams - Data Science, Operations & Engineering on unique machine learning system challenges at scale

- Leverage distributed training systems to build scalable machine learning pipelines including ETL, model training and deployments in Real-Time Bidding space. 

- Design and implement solutions to optimize distributed training execution in terms of model hyperparameter optimization, model training/inference latency and system-level bottlenecks  

- Research state-of-the-art machine learning infrastructures to improve data healthiness, model quality and state management during the lifecycle of ML models refresh.

- Optimize integration between popular machine learning libraries and cloud ML and data processing frameworks. 

- Build Deep Learning models and algorithms with optimal parallelism and performance on CPUs/ GPUs.

- Work with top management on defining teams goals and objectives.

Education

- MTech or Ph.D. in Computer Science, Software Engineering, Mathematics or related fields

Graas uses predictive AI to turbo-charge growth for eCommerce businesses. We are “Growth-as-a-Service”. Graas is a technology solution provider using predictive AI to turbo-charge growth for eCommerce businesses. Graas integrates traditional data silos and applies a machine-learning AI engine, acting as an in-house data scientist to predict trends and give real-time insights and actionable recommendations for brands. The platform can also turn insights into action by seamlessly executing these recommendations across marketplace store fronts, brand.coms, social and conversational commerce, performance marketing, inventory management, warehousing, and last mile logistics - all of which impacts a brand’s bottom line, driving profitable growth.

Roles & Responsibilities:

Work on implementation of real-time and batch data pipelines for disparate data sources.

• Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and AWS technologies.
• Build and maintain an analytics layer that utilizes the underlying data to generate dashboards and provide actionable insights.
• Identify improvement areas in the current data system and implement optimizations.
• Work on specific areas of data governance including metadata management and data quality management.
• Participate in discussions with Product Management and Business stakeholders to understand functional requirements and interact with other cross-functional teams as needed to develop, test, and release features.
• Develop Proof-of-Concepts to validate new technology solutions or advancements.
• Work in an Agile Scrum team and help with planning, scoping and creation of technical solutions for the new product capabilities, through to continuous delivery to production.
• Work on building intelligent systems using various AI/ML algorithms. 

Desired Experience/Skill:

• Must have worked on Analytics Applications involving Data Lakes, Data Warehouses and Reporting Implementations.
• Experience with private and public cloud architectures with pros/cons.
• Ability to write robust code in Python and SQL for data processing. Experience in libraries such as Pandas is a must; knowledge of one of the frameworks such as Django or Flask is a plus.
• Experience in implementing data processing pipelines using AWS services: Kinesis, Lambda, Redshift/Snowflake, RDS.
• Knowledge of Kafka, Redis is preferred
• Experience on design and implementation of real-time and batch pipelines. Knowledge of Airflow is preferred.
• Familiarity with machine learning frameworks (like Keras or PyTorch) and libraries (like scikit-learn)

• Partners with business stakeholders to translate business objectives into clearly defined analytical projects.
• Identify opportunities for text analytics and NLP to enhance the core product platform, select the best machine learning techniques for the specific business problem and then build the models that solve the problem.
• Own the end-end process, from recognizing the problem to implementing the solution.
• Define the variables and their inter-relationships and extract the data from our data repositories, leveraging infrastructure including Cloud computing solutions and relational database environments.
• Build predictive models that are accurate and robust and that help our customers to utilize the core platform to the maximum extent.

• 12 to 15 yrs of experience.
• An advanced degree in predictive analytics, machine learning, artificial intelligence; or a degree in programming and significant experience with text analytics/NLP. He shall have a strong background in machine learning (unsupervised and supervised techniques). In particular, excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, logistic regression, MLPs, RNNs, etc.
• Experience with text mining, parsing, and classification using state-of-the-art techniques.
• Experience with information retrieval, Natural Language Processing, Natural Language
• Understanding and Neural Language Modeling.
• Ability to evaluate the quality of ML models and to define the right performance metrics for models in accordance with the requirements of the core platform.
• Experience in the Python data science ecosystem: Pandas, NumPy, SciPy, sci-kit-learn, NLTK, Gensim, etc.
• Excellent verbal and written communication skills, particularly possessing the ability to share technical results and recommendations to both technical and non-technical audiences.
• Ability to perform high-level work both independently and collaboratively as a project member or leader on multiple projects.