Staff Machine Learning Engineer, Autopilot

As a Staff Machine Learning Engineer at Liftoff, your main responsibilities will include developing and maintaining advanced deep learning models for our Autopilot system. You'll also work closely with multidisciplinary teams to implement scalable solutions that enhance budget management capabilities for clients, ensuring that advertising budgets are allocated effectively across various platforms.

To qualify for the Staff Machine Learning Engineer position at Liftoff, candidates should possess at least 6 years of experience in Machine Learning, Software Engineering, or Infrastructure. A strong background in coding is essential, alongside a B.S. in Computer Science or a related field, with a PhD considered a plus. Previous experience in ad-tech and knowledge of ML frameworks such as PyTorch and PySpark will also be advantageous.

At Liftoff, a Staff Machine Learning Engineer will work with a variety of technologies, including AWS for cloud solutions, and frameworks such as PyTorch and PySpark for machine learning tasks. You will also engage in designing and implementing systems using in-house tools and open-source technologies, focusing on building robust and scalable models for advertising budget optimization.

The Autopilot system at Liftoff allows clients to set defined advertising budgets with daily spending limits. By intelligently pacing and optimizing ad opportunities throughout the day, it maximizes the performance and value derived from each ad dollar, ensuring that clients achieve higher spending efficiency and return on investment in their advertising strategies.

Yes, the Staff Machine Learning Engineer role at Liftoff is eligible for full-time remote work within certain states in the U.S. Liftoff embraces a remote-first work culture, providing flexibility and the opportunity for employees to collaborate virtually while engaging in essential in-person team gatherings regularly.

When answering this question, you should highlight specific machine learning models you have developed or enhanced, particularly those that apply to large-scale auction environments or budget optimization in advertising. Mention any results or impact your models have had on business outcomes.

Use this question to demonstrate your problem-solving skills. Discuss a real scenario where you encountered significant hurdles, the approach you took to overcome them, and the ultimate success of your solution, emphasizing how it relates to your role at Liftoff.

It's crucial to mention the methodologies you use to evaluate model bias, such as fairness metrics, and your processes for training data validation. Highlight any experience you have with bias audits or using diverse datasets to train your models more equitably.

Discuss your expertise in languages such as Python and Golang, stressing how your coding skills have been applied in building machine learning solutions. Make sure to relate these to specific frameworks or libraries you have used in your projects.

Share your strategies for effective communication and collaboration with team members from different backgrounds. Emphasize any tools or methods you use to ensure alignment and productivity in achieving shared goals.

Talk about your methods for continuous learning, such as following key publications, attending conferences, or participating in online courses. Mention any specific aspects of machine learning in ad-tech that particularly interest you.

Explain the various metrics you consider, such as accuracy, precision, recall, and business-related KPIs. Additionally, discuss how you might use A/B testing or other methods to evaluate model performance in a real-time bidding environment.

Provide a concise overview of a significant project where you led the design and implementation of a machine learning solution. Focus on the challenges, your leadership role, and the final results achieved, tailoring your response to align with the objectives at Liftoff.

Your answer should reflect your passion for the intersection of technology and marketing. You might talk about the dynamic nature of ad-tech, the opportunities for innovation it presents, and how it impacts the way businesses connect with consumers.

Discuss your openness to feedback as a vital part of personal and professional growth. Share how you have incorporated constructive criticism in the past to improve your machine learning models or work performance.