Machine Learning Engineer

Piano Overview

Piano’s Digital Experience Cloud empowers organizations to understand and influence customer behavior. By unifying customer data, analyzing behavior metrics and creating personalized customer journeys, Piano helps brands launch campaigns and products faster, strengthen customer engagement and drive personalization at scale from a single platform. Headquartered in Philadelphia with offices across the Americas, Europe and Asia Pacific, Piano serves a global client base, including Air France, the BBC, CBS, Kirin Holdings, Jaguar Land Rover and more. Piano has been recognized as one of the fastest-growing, most innovative technology companies in the world by World Economic Forum, Red Herring, Inc. and Deloitte. For more information, visit piano.io.

Position Overview

The open position is in the Piano Data Science team, a globally distributed team of bright and highly engaged data scientists and ML engineers. The Data Science team builds and operates ML-powered solutions for the various products Piano offers. The team is responsible for propensity modeling, natural language processing, LLM-powered question-answering and much more.

Areas of focus for the ML Engineer will include LLMs, NLP, production systems and real-time inference. The ML Engineer will also be responsible for ensuring that the data science solutions are robust and continuously optimized.

Keywords: Generative AI, LLMs, Python, Git, Machine Learning, Data Science, Propensity, NLP, OpenAI, Llama, Airflow, Kubernetes, Tensorflow, PyTorch, gRPC, PyArrow, pandas, numpy, FastAPI, Django, GraphQL

The role has the following responsibilities:

• Build and optimize solutions in areas such as LLMs, Natural Language Processing and Propensity Modeling, for example to achieve better scalability
• You will be part of the Data Science team and collaborate with other tech teams to ensure we identify opportunities to scale/optimize existing and upcoming solutions
• Explore and build tooling that would make the Data Science work more efficient
• Ensure that optimized solutions meet our requirements for reliability and robustness
• Design and implement new techniques and technologies in a production environment.
• Perform data-driven experiments on top of the existing infrastructure and products.

We are looking for candidates with the following qualifications:

• M.Sc. or a similar degree in Computer Science, Mathematics or other relevant field. Broad understanding of fundamentals in machine learning, natural language processing and statistics is required (relevant work experience may compensate for lack of formal education).
• Software engineering experience covering software engineering best practices (code quality, repo hygiene, code reviews, design documentation, continuous integration) is required.
• Machine Learning Engineer role experience (or similar) covering implementing and building ML-powered solutions in production is preferred. Experience with real-time predictions is a big plus.
• Strong problem-solving skills. Curiosity and practical orientation. Great collaboration and communication skills. Proven ability to understand state of the art techniques and emerging technologies and tools, and to deliver concrete results in a short time.
• Understanding of agile software development lifecycle and good programming skills.
• Mastery of Python is a requirement. Experience with frameworks such as PyTorch, Scikit-Learn, TensorFlow, HuggingFace Transformers or similar is a plus. Understanding Java is a plus. Experience with Airflow and Kubernetes is a plus.
• Ability to provide technical leadership and mentoring to other team members.
• Fluency in English.

We offer:

• An exciting technology stack with many opportunities for influencing which tools are used
• A great working environment in our office in Oslo
• Bright, skilled and passionate colleagues around the world
• Flexible schedule and working hours
• Competitive compensation

Applicants for this position should have authorization to work in this jurisdiction without sponsorship from the Company.