Senior ML Engineer

We are opening the search for our next Senior Machine Learning Engineer at Parable.

This person will be instrumental in building our core data science engine that transforms how companies understand and optimize their most precious resource - time. You will establish the foundation of our machine learning practice, working directly with our CTO and other senior engineers to develop sophisticated models that turn workplace data into meaningful insights about time and attention.

If you're excited about tackling one of society's most pressing problems - making time matter in a world that hijacks our attention - we'd love to talk.

This role is for someone who:

• Thrives at the intersection of experimentation and production. You're not just a researcher or just an engineer – you're both. You can rapidly prototype and iterate on models, but you also know how to build for scale and reliability. You have a track record of delivering results in one-third the time that most competent engineers think possible.

• Has deep expertise in machine learning techniques. You've spent years building and deploying various ML models, from classical supervised learning approaches to sophisticated neural networks and foundation models. You're always learning and experimenting with new methodologies, but you ground your work in proven techniques that deliver real value.

• Exercises extreme ownership. You take complete responsibility for your projects, cast no blame, and make no excuses. When you see a problem, you don't just point it out – you solve it. You're comfortable leading projects end-to-end, from initial concept to production deployment.

• Is obsessed with data and stays connected to the details. You understand that the quality of your models depends on the quality of your data and your deep understanding of it. You have a natural curiosity that drives you to explore patterns, anomalies, and edge cases that others might miss.

• Sees it as your obligation to challenge decisions when you disagree. You're not afraid to speak up when you have a different perspective, and you actively seek scrutiny of your own ideas. You believe that the best solutions emerge from thoughtful debate and collaborative problem-solving.

You will be responsible for:

• Developing our core data science engine that turns incoming data from a company's workplace stack into an understanding of time and attention

• Conceptualizing and applying various machine learning techniques to large data sets, from training neural networks to fine-tuning foundation models

• Building the foundations of ML at Parable – establishing the systems, methodologies, and practices that will shape our approach to solving customer problems

• Working closely with our CTO, AI and ML Engineers to deliver unique insights to customers

• Creating scalable and maintainable model architectures that can evolve with our product and customer needs

• Establishing metrics and processes for evaluating model performance and ensuring continuous improvement

In your first 3 months, you'll:

• Dive deep into customer data, proposing and testing methodologies to transform unstructured workplace data into meaningful insights about time usage

• Design and deploy neural networks and other ML models to generate actionable outcomes from complex data sets

• Establish our core machine learning infrastructure and development practices

• Ship multiple iterations of our models based on real customer feedback and data

• Experiment rapidly to deliver learnings and measurable results within the first month

• Collaborate with our product team to translate model outputs into valuable product features

Requirements:

• 5+ years of experience building and deploying machine learning models in production environments

• Strong expertise in Python

• Proven experience with machine learning frameworks (e.g., TensorFlow, PyTorch) and libraries (e.g., scikit-learn, pandas)

• Demonstrable experience with both supervised and unsupervised learning models, including deep learning, regression, and clustering techniques

• Proficiency in developing and deploying models in cloud-based environments (AWS, Azure, GCP)

• Strong ability to interpret and communicate data insights to non-technical stakeholders

• Experience with big data technologies (e.g., Hadoop, Spark) is desirable

• Master's degree or Ph.D. in Computer Science, Data Science, Statistics, or a related field is preferred