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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

Average salary estimate

$140000 / YEARLY (est.)
min
max
$120000K
$160000K

If an employer mentions a salary or salary range on their job, we display it as an "Employer Estimate". If a job has no salary data, Rise displays an estimate if available.

What You Should Know About Senior ML Engineer, Parable

Exciting news from Parable! We are on the hunt for a talented Senior Machine Learning Engineer to join our incredible team in Brooklyn. In this pivotal role, you'll help us create a state-of-the-art data science engine, revolutionizing the way companies grasp and enhance their most crucial resource: time. Your work will directly impact how we convert workplace data into valuable insights about time management and productivity. Picture this: you're collaborating closely with our CTO and senior engineers, not just working on models you’ve developed, but innovatively building systems that can scale and thrive in production settings. If you’re the type who relishes the seamless fusion of experimentation and real-world application, this is the spot for you! With your deep understanding of machine learning—from classical methods to advanced neural networks—you’ll spearhead projects that solve real-world challenges. We’re looking for someone who possesses a genuine passion for data, has a curious mind that seeks to uncover trends from raw datasets, and is unafraid to voice differing opinions to cultivate the best ideas. At Parable, you'll deliver impressive results, establish core ML practices, and transform data into actionable insights that matter. Join us as we tackle the intricate dynamics of time and attention in today’s fast-paced world. If you're ready to make a significant impact while working in a collaborative environment, we can’t wait to hear from you!

Frequently Asked Questions (FAQs) for Senior ML Engineer Role at Parable
What are the responsibilities of a Senior Machine Learning Engineer at Parable?

As a Senior Machine Learning Engineer at Parable, your core responsibilities will include developing our innovative data science engine, transforming incoming data into insights about time and attention. You will conceptualize and apply various machine learning techniques on large datasets, work alongside our CTO and other engineers to deliver unique insights, and create scalable model architectures. Additionally, you'll be responsible for evaluating model performance and ensuring continuous improvement across all models.

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What qualifications are required for the Senior ML Engineer position at Parable?

To qualify for the Senior Machine Learning Engineer role at Parable, candidates should have at least 5 years of experience in building and deploying machine learning models in production environments. Strong expertise in Python, combined with experience using ML frameworks like TensorFlow or PyTorch, is vital. A Master's degree or Ph.D. in fields like Computer Science or Data Science is preferred, along with proficiency in handling both supervised and unsupervised learning models.

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What kind of projects will the Senior Machine Learning Engineer work on at Parable?

The Senior Machine Learning Engineer at Parable will work on various complex projects focusing on transforming workplace data into valuable insights regarding time utilization. This includes designing and deploying neural networks, establishing core machine learning practices, building scalable architectures, and iterating models based on customer feedback. Your work will directly contribute to helping clients optimize their time management and understand attention dynamics in their organizations.

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What skills are important for a successful Senior Machine Learning Engineer at Parable?

Key skills for a Senior Machine Learning Engineer at Parable include deep expertise in machine learning algorithms, proficiency in Python, and familiarity with frameworks such as TensorFlow or PyTorch. Strong analytical abilities to interpret data insights and excellent communication skills for conveying complex concepts to non-technical stakeholders are essential. Additionally, experience in cloud services and big data technologies will further enhance a candidate’s capability to excel in this role.

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What can a new hire expect during their first few months as a Senior ML Engineer at Parable?

During the first three months as a Senior Machine Learning Engineer at Parable, you will dive deep into customer data, propose methodologies, and design models that yield actionable outcomes. You'll establish the core machine learning infrastructure, collaborate closely with product teams, and ship iterative models based on user feedback. This role encourages rapid experimentation, ensuring that you make measurable contributions right from the start.

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Common Interview Questions for Senior ML Engineer
Can you describe your experience with deploying machine learning models in production?

When answering this question, outline specific projects where you successfully deployed machine learning models. Discuss the tools and frameworks you used, the challenges you faced during deployment, and how you ensured model reliability and scalability. Highlighting metrics that demonstrate success can also be very impactful.

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What machine learning techniques are you most proficient in?

Your response should include a mixture of classical and advanced machine learning techniques you are experienced with, such as supervised and unsupervised learning models. Be specific about cases where you applied these techniques for real-world problems and the outcomes of those projects.

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How do you ensure the quality of your models?

To ensure model quality, focus on data quality assessments, rigorous testing, and continuous evaluation. Discuss methodologies you employ, such as cross-validation, metric establishment, and incorporating feedback from stakeholders to refine and improve model performance over time.

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How do you handle disagreements with team members regarding technical decisions?

In your response, emphasize open communication and the importance of constructive debate. Explain how you actively invite feedback on your ideas, listen to differing perspectives, and work collaboratively to arrive at a solution that serves the best interests of the project.

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Could you give an example of how you transformed a complex dataset into actionable insights?

Outline a specific project where you took a complex dataset, applied techniques to analyze it, and extracted insights that led to a tangible impact on business decisions. Include details about the methodologies used and how you communicated those insights to non-technical stakeholders.

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What cloud services have you utilized in your machine learning projects?

Mention the cloud platforms you've used, such as AWS, Azure, or GCP, and describe specific services related to machine learning you have leveraged, like deployment strategies or serverless computing. Your experience with cloud-based systems shows your ability to work within modern infrastructure and enhances your candidacy.

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How do you approach learning new machine learning techniques or frameworks?

Describe your commitment to continuous learning, including resources such as online courses, webinars, and industry publications. Discuss how you apply new techniques in small projects or personal experiments, making it clear that you value staying current with the rapidly evolving field.

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Explain a time when you delivered results quickly under tight deadlines.

Provide an example where you successfully met a challenging deadline while maintaining quality. Emphasize your project management skills, efficient workflow strategies, and ability to prioritize effectively, narrating how your results positively affected the project.

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What is your experience with big data technologies?

Highlight your familiarity with big data technologies like Hadoop or Spark and detail how you've used them in past projects. Discuss specific results achieved through the application of these technologies in processing or analyzing data.

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What methodologies do you use for model evaluation?

Outline various evaluation methodologies, such as cross-validation, confusion matrix, ROC-AUC, and precision-recall strategies. Discuss how these methodologies help you assess model performance and ensure that the models you champion are not only effective but applicable in real-world scenarios.

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Since 1985, The Parable Group has helped independent stores buy, promote and sell products using data, technology and marketing expertise in the Christian market. The Parable Group is a leading retail services provider specializing in print and di...

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Full-time, on-site
DATE POSTED
March 26, 2025

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