SparkCognition catalyzes sustainable growth for our clients throughout the world with proven artificial intelligence (AI) systems, award-winning machine learning technology, and a multinational team of AI thought leaders. Our clients are trusted with advancing lives, infrastructure, sustainability, and financial systems across the globe. They partner with SparkCognition to understand their industry's most pressing challenges, analyze complex data, empower decision-making, and transform human and industrial productivity with scalable AI solutions to solve the problems that matter most. With our leading-edge artificial intelligence products and solutions, our clients can adapt to a rapidly changing digital landscape, accelerate their business strategies, and reduce environmental impact creating a better, smarter, and more sustainable world.
We are looking for a Machine Learning Engineer to join our team and help create the next generation of AI solutions. In this role, you will work alongside other engineers and data scientists to develop, scale, and deliver cross-cutting ML technologies that make up the heart of our AI product portfolio.
If you were already working for us, you would be:

• Working closely with data scientists to understand core challenges and develop solutions which improve data science productivity
• Collaborating with data scientists and engineers to transform state-of-the-art ML prototypes and algorithms into production-ready solutions
• Distilling complex ML pipelines into their underlying components with an eye towards efficiency, reusability, and repeatability
• Working within a highly collaborative and cross-functional team to advance our product portfolio and drive the evolution of our common ML infrastructure
• Applying your experience to mentor other team members and make intelligent, forward-thinking technical decisions that intersect multiple products/teams

You may be a great fit for our team if you have:

• A bachelor's degree or higher in Computer Science or a related field
• A proven ability to deliver quality ML models in a production environment
• A thorough understanding of the data science process, including data processing and analysis; feature extraction and engineering; and model training and evaluation
• Working knowledge of standard ML approaches and algorithms, including but not limited to Linear Models, Decision Trees, Clustering, and Neural Networks
• A strong command of Python and at least one production OOP language (C#, Java, Scala)
• A working understanding of database fundamentals and technologies
• Hands-on experience with common ML toolkits such as sci-kit learn and at least one deep learning framework (preferably PyTorch or Tensorflow)
• An ability to break down complex or ambiguous problems into manageable tasks
• Excellent communication skills, with the ability to explain ML solutions to developers and team members, regardless of their technical background

Exceptional candidates may also have:

• Experience developing algorithms in a distributed environment (e.g., Dask, Ray, etc.)
• Experience with workflow management and orchestration technologies such as Kubeflow or Airflow
• Experience with the MLOps life cycle, especially as it pertains to practical implementation within a cloud (Azure, AWS, GCP) or cloud-agnostic environment