Applied ML Engineer

As an Applied Machine Learning Engineer at Splash, you will design and implement ML algorithms specifically aimed at enhancing our music creation tools. Your responsibilities will include identifying off-the-shelf AI solutions, running models in production, maintaining production code, and handling data pipelines for audio and unstructured data effectively. You'll also work on tasks like audio analysis and melody generation, ensuring that our products provide a great user experience.

To qualify for the Applied Machine Learning Engineer role at Splash, you should have proven software development experience, ideally with Python. Familiarity with the PyTorch framework for deploying ML models and experience in AWS for scaling these solutions are essential. A strong understanding of audio technology processes, as well as clear communication skills, will be pivotal for collaborating with our diverse teams.

Your understanding of music production or audio technology will be invaluable as an Applied Machine Learning Engineer at Splash. It allows you to contextualize user problems and tailor machine learning solutions that resonate better with our audience. Your passion for music will also help you engage constructively with your team in refining our music creation tools.

At Splash, the team dynamic is lively and collaborative. As an Applied Machine Learning Engineer, you’ll be part of a small, dynamic group comprised of industry veterans and passionate creatives. You’ll have the opportunity to work closely with various departments, ensuring seamless integration of your ML innovations while engaging with team members who share a deep passion for music.

At Splash, our commitment to diversity, equity, and inclusion is not just a statement; it's embedded in our culture. As an Applied Machine Learning Engineer, you will be part of a supportive environment that values different backgrounds and perspectives, driving creativity and success. We believe that a diverse workforce leads to better problem-solving and innovation, particularly in music technology.

When designing ML algorithms for music technology, it's essential to first identify user pain points. I typically start with thorough user research to understand the needs and then design algorithms that enhance features, such as audio analysis or melody generation, based on those insights. Being familiar with existing processes and available tools can also inform an efficient design strategy.

Deploying ML models in production requires a systematic approach to ensure accuracy and reliability. My experience includes versioning models properly, conducting A/B testing for performance evaluation, and implementing domain shift testing to adapt to changes in data patterns. I also prioritize maintaining clean, production-ready code to facilitate ongoing iterations and improvements.

For audio data preprocessing, I typically use techniques such as normalization, feature extraction, and segmentation to prepare the data for machine learning. I also focus on cleaning the data to remove any noise that could affect the model's performance, and I employ batch processing to manage large datasets while ensuring consistency between training and testing data.

In a recent project, I collaborated closely with Product and Engineering teams to refine a music recommendation system. We conducted regular meetings to align on the project goals, with me demonstrating how ML can enhance user experience. This collaborative effort ensured that the final product not only met technical specifications but was also user-friendly and effective.

I stay updated by subscribing to relevant journals, attending industry conferences, and participating in online forums focused on music and AI. Networking with other professionals in the field and continuous learning through workshops and online courses have also proven invaluable in keeping me informed about emerging trends and technologies.

Evaluating the effectiveness of an ML model requires comprehensive metrics. I typically rely on precision, recall, and F1-score to measure performance within music applications. Additionally, I validate models using cross-validation techniques and conduct user feedback sessions to understand real-world applicability and user satisfaction, which are vital for success in the music domain.

When implementing off-the-shelf ML tools, I assess their compatibility with our existing systems and the specific problem we're solving. I look for solutions that can be integrated smoothly with minimal disruption. In the music tech context, I also prioritize tools that enhance user engagement and are innovative in solving real-world problems.

Ensuring ethical AI practices involves implementing fairness audits and transparency in model training and testing phases. I also prioritize bias detection during the data selection process to ensure our models reflect a broad range of user experiences. At Splash, I would emphasize collaborative efforts to uphold values around responsible AI usage, especially in creative applications.

When working independently, I prioritize time management by setting milestones and breaking tasks into manageable chunks. Utilizing tools for task tracking and regular check-ins with the team helps me stay accountable. Additionally, I allocate time for continuous learning to ensure I'm improving my skills while progressing through project deliverables.

AI has a transformative impact on the music industry, enhancing creativity and making music production more accessible. It enables artists to explore new sounds and streamline tedious processes. I believe that the future of music will incorporate AI as a partner in creativity, and it's exciting to be at the forefront of these advancements at Splash, where we embrace and challenge these boundaries.