SoundCloud empowers artists and fans to connect and share through music. Founded in 2007, SoundCloud is an artist-first platform empowering artists to build and grow their careers by providing them with the most progressive tools, services, and resources. With over 400+ million tracks from 40 million artists, the future of music is SoundCloud.
We are looking for aSenior Machine Learning Engineerto join ourAds team. As part of the Revenue Group, our mission is to provide anyone on any budget access to the music and communities they love through tailored and sustainable monetization and user engagement.
The Ads team at SoundCloud operates as a cross-domain unit that collaborates closely with various departments to optimize the advertising experience for listeners while maximising revenue potential. By carefully balancing user engagement with strategic ad placement, the team works tirelessly to build a sustainable platform that provides consistent and meaningful revenue streams for artists across the platform.
Key Responsibilities:
As a Senior Machine Learning Engineer, you will work closely with Scientists to move ML projects from ideation to production. This includes designing, building, evaluating, and deploying scalable models that directly impact the experience of millions of users globally.
You’ll lead the end-to-end development process, from architecture design to model deployment and monitoring. Beyond technical implementation, you’ll set engineering standards, mentor teammates, and influence strategic decisions. You’ll champion best practices for testing, reliability, and long-term maintainability of ML systems and infrastructure, raising the engineering bar across SoundCloud.
We value your technical leadership, engineering craftsmanship, and passion for building high-quality ML products that drive meaningful and lasting impact.
Experience and Background:
Proven track record of successfully releasing large-scale ML models to production, including ownership of model design, training/ fine-tuning, evaluation, and optimization of both training and inference performance. Familiarity with state-of-the-art recommendation systems and large language models (LLMs) is a strong plus
Demonstrated experience building and scaling robust ML infrastructure and tooling, including components such as deployment automation, model lifecycle management, monitoring, containerization, and orchestration. Proficiency with cloud platforms (e.g., GCP, AWS, Azure) is expected
Strong background of professional software engineering practices, including version control, test-driven development, peer reviews, CI/CD, and clean code principles across the ML system lifecycle
Ability to clean, process, and analyze large datasets, as well as experience conducting and interpreting A/B tests using SQL
Ability to make informed build-or-buy decisions by evaluating technical trade-offs, integration complexity, long-term maintenance, and business impact
Expert-level coding skills in Python, Scala, Java, or similar languages, with deep hands-on experience in ML frameworks (e.g., TensorFlow, PyTorch) and distributed data systems (e.g., Spark, BigQuery)
Effective communicator and technical collaborator who proactively drives initiatives in cross-functional, agile teams. Comfortable mentoring peers, leading discussions around architecture and design, and delivering impactful, scalable solutions
Preferred Experience:
Demonstrated expertise in spearheading the end-to-end development of machine learning solutions specifically within the advertising industry, including system architecture design, implementation, and production deployment focusing on reliability and scalability
Experience in designing and deploying (including security, reliability and scale) of ML solutions on the cloud, such as AWS or GCP
Experience building solutions involving large datasets and/or ML models using distributed computing frameworks and technologies
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