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Lead Data/ML Engineer

SuperAds is seeking a Lead Data/ML Engineer to design and optimize scalable data pipelines and integrate advanced machine learning models into production. In this role, you’ll work at the intersection of data engineering and AI, enabling marketers with cutting-edge creative analytics.



As part of our innovative SuperAds team, you’ll operate with startup agility while leveraging the stability of Superside. Reporting directly to the CTO, you’ll play a pivotal role in building robust, cost-efficient systems that scale with rapid growth and revolutionize ad performance analysis.


What you’ll do:
  • Design and maintain scalable ETL pipelines for data integration from platforms like YouTube, Google Ads, and Pinterest, ensuring seamless data ingestion and high-quality results.
  • Optimize data syncing algorithms to handle large datasets efficiently, improving scalability and performance.
  • Collaborate with AI researchers to transform machine learning models into production pipelines, delivering actionable insights in real time.
  • Implement automated testing, monitoring, and validation processes to ensure data reliability and accuracy.
  • Manage and optimize cloud infrastructure (e.g., AWS, GCP), focusing on cost efficiency and resource scalability.
  • Build fault-tolerant systems to support high data volumes and ensure platform stability under heavy usage.
  • Research and adopt emerging technologies to continuously improve data workflows and ML deployment.
  • Troubleshoot and resolve technical challenges quickly and effectively.
  • Work closely with product and engineering teams to align on technical goals and ensure seamless integration.
  • Document best practices and mentor junior engineers, fostering knowledge sharing and team development.


What you’ll need to succeed:
  • 4+ years of experience in data engineering roles with expertise in building and maintaining complex ETL pipelines.
  • Strong programming skills in Python, with a deep understanding of system engineering and data infrastructure design.
  • Experience deploying machine learning models in production environments and integrating them into scalable data pipelines.
  • Proficiency with AI technologies such as PyTorch, TensorFlow, or Jax is a strong advantage.
  • Solid knowledge of distributed systems, data modeling, and storage solutions for high-volume, real-time data.
  • Familiarity with orchestration tools (Airflow, Temporal) and containerization (Docker, Kubernetes) for managing workflows.
  • Proficiency with cloud platforms like AWS, GCP, Snowflake, or Databricks, including cost-effective resource management.
  • Knowledge of ad-tech/mar-tech platforms and data integration from external APIs and large datasets.
  • Strong problem-solving skills, with the ability to troubleshoot complex data issues across pipelines and integrations.
  • Excellent collaboration and communication skills, with comfort working in cross-functional teams.



About Superside


Superside is a revolutionary way for businesses to get good design done at scale. Trusted by 450+ ambitious companies, Superside makes design hassle-free for marketing and creative teams. By combining the top 1% of creative talent from around the world with purpose-built technology and the rigor of design ops, Superside helps ambitious brands grow faster. Since inception, Superside has been a fully remote company, with more than 700 team members working across 57 countries and 13 timezones.


Learn more at superside.com


Diversity, Equity and Inclusion


We’re an equal opportunity company. All applicants will be considered regardless of ethnicity, appearance, religion, gender identity, sexual orientation, national origin, veteran or disability status.

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What You Should Know About Lead Data/ML Engineer, super.AI

SuperAds is on the lookout for an extraordinary Lead Data/ML Engineer to join our dynamic team! If you're passionate about crafting and optimizing data pipelines while integrating cutting-edge machine learning models into production, this is the opportunity for you. In your new role, you'll be at the exciting junction of data engineering and artificial intelligence, empowering marketers with advanced creative analytics. At SuperAds, we embrace the agile spirit of a startup, complemented by the reliability of Superside. You’ll be reporting directly to the CTO and playing a crucial role in developing robust, cost-efficient systems that will reshape ad performance analysis. Your day-to-day will involve designing scalable ETL pipelines for adept data integration from prominent platforms like YouTube, Google Ads, and Pinterest, which will guarantee seamless data ingestion. You’ll collaborate closely with AI researchers, transforming sophisticated machine learning models into production-ready pipelines, all while ensuring the highest data quality through automated testing and monitoring. If you're a proactive problem-solver and have a good grasp of cloud infrastructures like AWS or GCP, we’d love to hear from you! Your experience with containerization tools and a knack for mentoring junior engineers will take our projects to new heights. We pride ourselves on being a fully remote company, offering you the flexibility to work from anywhere while making impactful contributions to the advertising world. Join us at SuperAds, and let’s create magic together!

Frequently Asked Questions (FAQs) for Lead Data/ML Engineer Role at super.AI
What are the responsibilities of a Lead Data/ML Engineer at SuperAds?

As a Lead Data/ML Engineer at SuperAds, your primary responsibilities will include designing and managing scalable ETL pipelines for data integration, optimizing data syncing algorithms for large datasets, and transforming machine learning models into production pipelines. You'll also implement automated testing and monitoring processes, manage cloud infrastructure for cost efficiency, and troubleshoot technical challenges. Additionally, mentoring junior engineers and collaborating with different teams are key aspects of this role.

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What qualifications do you need to apply for the Lead Data/ML Engineer position at SuperAds?

To be considered for the Lead Data/ML Engineer position at SuperAds, you should have at least 4 years of experience in data engineering, with solid expertise in ETL pipeline construction. Strong programming skills in Python, experience in deploying machine learning models in production, familiarity with cloud platforms like AWS or GCP, and knowledge of data orchestration tools are also essential. A passion for problem-solving and excellent communication skills are crucial as well.

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What tools and technologies should a Lead Data/ML Engineer be proficient in at SuperAds?

A Lead Data/ML Engineer at SuperAds should be proficient in tools and technologies such as Python for programming, orchestration tools like Airflow and Temporal, and containerization technologies including Docker and Kubernetes. Experience with AI frameworks such as PyTorch, TensorFlow, or Jax is advantageous. Knowledge of cloud platforms like AWS and GCP, as well as familiarity with data integration from external APIs, will also greatly benefit the role.

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How does the role of Lead Data/ML Engineer at SuperAds contribute to ad performance analysis?

The Lead Data/ML Engineer at SuperAds plays a vital role in enhancing ad performance analysis. By designing scalable data pipelines that ensure reliable data ingestion from major platforms, optimizing machine learning model integration for real-time insights, and maintaining data quality through automated processes, you directly influence the effectiveness of marketing strategies. This ultimately helps drive better results for our clients.

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What opportunities for growth are available for a Lead Data/ML Engineer at SuperAds?

At SuperAds, we offer numerous growth opportunities for a Lead Data/ML Engineer. You will have access to mentoring opportunities, professional development programs, and the chance to work on groundbreaking technologies in a rapidly evolving field. Additionally, as you contribute to high-impact projects and collaborate with cross-functional teams, you’ll build leadership skills that can pave the way for future career advancement within the company.

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Common Interview Questions for Lead Data/ML Engineer
Can you describe your experience with designing and maintaining ETL pipelines?

When answering this question, highlight specific projects where you've designed ETL pipelines. Discuss the tools and technologies used, any challenges faced during the process, and how you ensured data quality and efficiency. Providing metrics or outcomes can demonstrate the impact of your work.

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How do you approach optimizing data syncing algorithms?

Explain your process of evaluating current algorithms and identifying bottlenecks. Discuss strategies you might employ, such as adjusting query parameters or implementing caching solutions, to enhance performance. Providing concrete examples of past optimizations will strengthen your answer.

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What techniques do you use for deploying machine learning models to production?

Discuss the full lifecycle of deploying ML models, from initial development to deployment and monitoring. Explain how you handle version control, testing, and integration with existing data pipelines. Mention any specific tools or frameworks you've utilized in the deployment process.

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Can you give an example of a technical challenge you've faced and how you resolved it?

Answer this question with a specific scenario. Briefly narrate the context, the challenges faced, and the steps taken to resolve the issue. Conclude by highlighting the results or learnings from the experience, which showcases your problem-solving skills.

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What cloud platforms are you familiar with, and how have you optimized resource usage?

In your response, mention the specific cloud platforms you've worked with, such as AWS or GCP. Discuss particular services you've utilized and any strategies you've implemented for cost efficient resource management, like auto-scaling or spot instances.

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How do you stay current with emerging technologies relevant to data engineering?

Highlight your commitment to professional growth. Discuss how you engage with the data engineering community through online courses, webinars, blogs, or conferences. Mention any recent tools or technologies you’ve explored that could benefit the role at SuperAds.

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What role does documentation play in your projects?

Emphasize the importance of documentation in ensuring clarity and knowledge sharing within teams. Discuss specific practices you follow for maintaining comprehensive documentation, which can enhance collaboration and assist in onboarding new team members.

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How do you prioritize tasks when working with cross-functional teams?

Explain your approach to prioritization, which may include aligning objectives with stakeholders and using project management tools. Discuss the importance of clear communication and setting realistic timelines to meet the goals of all parties involved.

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What experience do you have with mentoring junior engineers?

Share your mentoring philosophy and any specific mentoring experiences. Discuss how you provide guidance on technical skills and promote a collaborative work environment. Mention how mentoring contributes to team development and your own growth as a lead.

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What excites you about the intersection of data engineering and machine learning?

Express your enthusiasm for this cutting-edge field by discussing its potential to drive innovation. Share specific examples or trends you find compelling, and how they align with your goals as a Lead Data/ML Engineer at SuperAds.

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Full-time, remote
DATE POSTED
January 8, 2025

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