Job Description
Title: Senior Data Engineer, Applied Machine Learning
Location: Remote
JobDescription:Who We Are: WellSaid Labs
Were creating Voice for everyone.
At WellSaid Labs, we enable creatives around the globe by putting high-tech, human parity technology into their hands, giving them the ability to add voice-over to any project and iterate with ease. Creative teams use WellSaid Labs Voice Studio to create compelling employee training, design unique digital experiences, and narrate audiobooks. We believe deeply in AI for Good, and that technology should be empowering, engaging, and fair to all people.Who You Are: an Experienced Data Engineer working in Applied Machine Learning
The WellSaid Labs Applied Machine Learning Team works with stakeholders to identify, refine, and solve problems at the intersection of machine learning and customer needs. This team understands customer needs through quantitative and qualitative research and they work across WSL teams to understand how machine learning is utilized and how it can be improved.
Improving and maintaining our ML solutions includes creating test datasets and metrics to define and gauge success, working with the ML Platform Team to prioritize model updates, training new models for deployment, coordinating releases, and educating the customer on any new capabilities. The Applied ML Team is consistently testing and reviewing any deployed models.
As a Senior Data Engineer on the Applied ML Team at WellSaid Labs, you will be working to regularly improve our ML services, chiefly our text-to-speech service. You will own the strategy and development of projects pertaining to our testing frameworks, customer research, and model improvements, among others. As an individual contributor you will also add new datasets; train, deploy, and evaluate new models; and design experiments and algorithms for solving new and creative TTS challenges.
You will build automated systems for evaluating ML performance: accuracy, consistency, and customer acceptance. You will summarize your findings into compelling reports and then collaborate with the Platform and Applied ML teams to build solutions. You should be familiar with Text-to-Speech technology, database querying, data labeling and preparing, crafting workflows for crowd-sourcing evaluations, and metrics reporting. It would be great if you have experience being a team leader and can advise the rest of the Applied ML team in ML-, data- and research-related questions and proposals.How Youll Contribute:
In your day-to-day, you will:
- Own, strategize, and manage efforts to evaluate and improve the model: gather customer insights, audit training data, and bridge gaps between model performance and customer expectations
- Work directly with text and audio data: gathering, compiling, and organizing datasets, preparing data for machine training, evaluating results, debugging problematic data
- Train and deploy ML models: incorporating new data, monitoring training metrics, debugging failing code, deploying a model for customer use
- Evaluate ML models: consider causation or correlation between training data and ML predictions, design ML experiments and establish success criteria, gather and evaluate metrics including mean opinion scores; design evaluation tools for measuring pronunciation accuracy, naturalness, text normalization coverage, among others to minimize customer-model friction
- Additional research projects: interesting data or use cases, alternative services and solutions, internal process improvements, new quality evaluation exercises, etc.
In this role, you will need to take big ideas and build out specifications for achieving smaller milestones toward the ideal state. You will need to organize efforts into scrappy research, MVP executions, and long-term solutions. You should balance immediate results and technical debt with vision for ideal, automated systems. Additionally, this role requires you to write and review code that enables you, and others, to perform each of these tasks. It also requires you to think critically about Text-to-Speech tooling, customer experience, language, dialect, pronunciation, phonemics, and audio dynamics in order to build the highest quality voices and Studio/API experiences for our customers.What Were Looking For
To thrive in this role, you ideally have experience with and a solid understanding of ML concepts and best practices, a history of successfully managing datasets and metrics in a ML capacity, coding experience developing tools that can evaluate data and enable you and your team to establish recommendations based on data analysis results, and experience with software releases to production with strong considerations for both customer impact and ethical implementations of AI.
- You have worked in ML pipelines and have experience developing test suites, automating testing frameworks, and feeding data analysis back into model development
- You have experience working with audio data or within the TTS domain
- You have worked in a technical team at a high level, managing project expectations and communicating your plans, project statuses, and results frequently and comprehensively
- You have worked with a wide array of data types, building analysis tools and establishing success criteria for evaluating the success of data-driven projects
- You have built and deployed ML models for use by a non-technical audience, clearly communicating usage guidelines and best practices
- You have experience building and documenting new processes, especially in a ML pipeline or similar capacity
- You have a strong understanding of the importance of data preparation for ML training, data visualization and metrics for ML assessment, and analysis of ML results
- You have familiarity with software and feature releases and can work closely with a Product team for exposing ML changes to customers
- [Bonus] You are fluent in Spanish or French
- [Bonus] You have studied Deep Learning and have applied models to solve technical challenges
- [Bonus] You have an interest in eventually managing an Applied ML Team, strategizing workload, and mentoring contributing engineers under your supervision