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ML/AI Engineer - Infrastructure

liETtVLaARqgmMEbYzHNNLIzUPcdfPrwhYtVK7Qa.png Fast Facts

Figma is seeking an ML/AI Engineer to develop and enhance machine learning infrastructure and tools, improving their design and collaboration products.

liETtVLaARqgmMEbYzHNNLIzUPcdfPrwhYtVK7Qa.png Responsibilities: You will build infrastructure for model training, deployment, and serving, collaborate with ML engineers on internal tooling, and work with applied research and product engineers to enhance Figma's offerings.

liETtVLaARqgmMEbYzHNNLIzUPcdfPrwhYtVK7Qa.png Skills: Required skills include a strong background in backend or infrastructure engineering, expertise in machine learning or data infrastructure, and experience in deploying ML models successfully.

liETtVLaARqgmMEbYzHNNLIzUPcdfPrwhYtVK7Qa.png Qualifications: Preferred qualifications include a history of planning multi-year roadmaps and experience mentoring or influencing senior engineers.

liETtVLaARqgmMEbYzHNNLIzUPcdfPrwhYtVK7Qa.png Location: This role is located in the United States, specifically in San Francisco or New York, with option for remote work.

liETtVLaARqgmMEbYzHNNLIzUPcdfPrwhYtVK7Qa.png Compensation: Not provided by employer. Typical compensation ranges for this position are between $130,000 - $180,000.



We’re looking for engineers with a Machine Learning and Artificial Intelligence background to improve our products and build new capabilities. You will be building the core infrastructure to serve and deploy models efficiently, as well as world-class tooling that enables us to iterate on models quickly. . You will be combining industry best practices and a first-principles approach to design and build ML infrastructure that will improve Figma’s design and collaboration tool.

What you’ll do at Figma:

  • Build infrastructure to train, deploy, and serve models at scale
  • Partner directly with ML engineers to build internal tools for model development and experimentation
  • Work in concert with applied research and product engineers to improve Figma’s design and collaboration tool.
  • Combine industry best practices and a first-principles approach to design and build ML infrastructure.

We'd love to hear from you if you have:

  • 5+ years of backend or infrastructure experience.
  • 3+ years of experience in one or more of the following areas: machine learning, machine learning infrastructure, data infrastructure.
  • Proven experience deploying ML models and implementing the full lifecycle of model development.
  • Experience communicating and working across functions to drive solutions.

While not required, It’s an added plus if you also have:

  • Proven track record of planning multi-year roadmap in which shorter-term projects ladder to the long-term vision.
  • Experience in mentoring/influencing senior engineers across organizations.

At Figma, one of our values is Grow As You Go. We believe in hiring smart, curious people who are excited to learn and develop their skills. If you’re excited about this role but your past experience doesn’t align perfectly with the points outlined in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles.

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Average salary estimate

$155000 / YEARLY (est.)
min
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$130000K
$180000K

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 ML/AI Engineer - Infrastructure, Figma

At Figma, we're on the lookout for a talented ML/AI Engineer - Infrastructure to join our innovative team! This is an exciting remote opportunity where you'll play a vital role in developing and enhancing our machine learning infrastructure and tools. Your primary mission will be to improve the design and collaboration products that our users love. Imagine building the core infrastructure that allows us to efficiently train, deploy, and serve models at scale. You'll partner closely with ML engineers to create internal tools that enhance model development and experimentation, ensuring that our offerings are always at the forefront of technology. Your expertise will not only help streamline processes but will also directly contribute to the improvement of Figma’s design tools. If you have a strong background in backend or infrastructure engineering, along with experience in machine learning and deploying models successfully, we want to hear from you! This role does require a minimum of 5 years of experience in backend or infrastructure work, plus at least 3 years in machine learning or data infrastructure. If you're eager to grow and thrive in a collaborative environment, and you're passionate about combining industry best practices with a first-principles approach, then you might just be the perfect fit. Join us at Figma, where we believe in continuous learning and development, and where your ideas can truly make a difference in the evolving world of design collaboration.

Frequently Asked Questions (FAQs) for ML/AI Engineer - Infrastructure Role at Figma
What are the key responsibilities of the ML/AI Engineer - Infrastructure role at Figma?

The ML/AI Engineer - Infrastructure at Figma is responsible for building infrastructure to train, deploy, and serve machine learning models at scale. You will collaborate with ML engineers to develop internal tools and work closely with applied research teams to enhance our design and collaboration products.

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What qualifications do I need to apply for the ML/AI Engineer - Infrastructure position at Figma?

To qualify for the ML/AI Engineer - Infrastructure role at Figma, candidates should have over 5 years of experience in backend or infrastructure engineering, and at least 3 years focused on machine learning or data infrastructure. Proven experience with deploying ML models is also essential.

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What is the work environment like for an ML/AI Engineer - Infrastructure at Figma?

The ML/AI Engineer - Infrastructure role at Figma is a remote position, allowing flexibility in your work environment. You will be part of a collaborative team that values innovation, sharing knowledge, and growing together in the fast-paced world of design technology.

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What specific skills should I have for the ML/AI Engineer - Infrastructure role at Figma?

A strong background in backend or infrastructure engineering is crucial, along with expertise in machine learning or data infrastructure. Experience in deploying ML models successfully and the ability to communicate across functions are also important for this role.

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How does Figma support the growth of the ML/AI Engineer - Infrastructure?

At Figma, we prioritize personal and professional growth, encouraging our employees to learn and develop their skills through collaboration, mentorship, and innovative projects that align with their interests and career goals.

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Common Interview Questions for ML/AI Engineer - Infrastructure
Can you describe your experience with machine learning model deployment?

When answering this question, highlight specific projects where you successfully deployed machine learning models. Discuss the frameworks and tools used, any challenges encountered, and how you ensured models perform optimally in production.

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What infrastructure design principles do you follow when building ML systems?

Articulate your understanding of scalability, reliability, and maintainability in infrastructure design. Mention best practices, such as using microservices, containerization, and continuous integration/continuous deployment (CI/CD) pipelines.

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How do you approach collaboration with product engineers and researchers?

Illustrate your teamwork skills and communication strategies. Describe how you facilitate discussions to understand needs, foster innovation, and ensure that the engineering efforts align seamlessly with product goals.

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What tools and technologies have you utilized in machine learning infrastructure?

Be specific about the tools you’ve worked with, such as TensorFlow, Kubernetes, or AWS services. Explain how you've used these tools to improve infrastructure efficiency and model performance.

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Describe how you would troubleshoot an ML model that is underperforming.

Discuss a systematic approach to debugging, including analyzing data quality, reviewing feature selection, model hyperparameters, and potentially retraining models based on insights gathered.

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What strategies have you employed to mentor or influence other engineers?

Share examples of how you’ve taken initiative to guide others—perhaps by leading workshops, sharing resources, or participating in code reviews. Highlight the outcomes of your mentoring.

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How do you prioritize tasks when working on multiple ML projects?

Discuss your time management strategies and methodologies (like Agile or Kanban) that help you balance competing priorities while ensuring project requirements are met within deadlines.

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What do you believe is the future of machine learning infrastructure?

Share insights on emerging trends, such as the integration of more automated tools, the push toward edge computing, and advancements in model interpretability that will shape the future landscape.

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Can you describe a time you had to advocate for a specific technology or architecture choice?

Provide a narrative that showcases your ability to persuade stakeholders by presenting strong data-backed arguments. Be specific about the contexts, your reasoning, and the results that followed.

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What motivates you to work in the field of machine learning?

Be genuine about your passion for technology and innovation. Discuss how the transformative potential of ML in solving real-world problems inspires you and aligns with your career aspirations.

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CULTURE VALUES
Empathetic
Collaboration over Competition
Growth & Learning
Passion for Exploration
Fast-Paced
Startup Mindset
Diversity of Opinions
Rise from Within
SENIORITY LEVEL REQUIREMENT
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Full-time, remote
DATE POSTED
April 9, 2025

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