Experience Requirements:

• 3+ years

Experience Requirements:

• 3+ years

Experience Requirements:

• 1+ years

Education Requirements:

• Bachelor’s degree in Computer Science, a related field or equivalent experience

Experience Requirements:

• 4+ years of experience analyzing data in a fast-paced, data-driven environment with proven ability to apply scientific methods to solve real-world problems on web-scale data

• Extensive experience solving analytical problems using quantitative approaches including in the fields of Machine Learning, Statistical Modeling, Forecasting, Econometrics or other related fields

• Experience using machine learning and deep learning frameworks, such as PyTorch, TensorFlow or scikit-learn

• Ability to manipulate large data sets with high dimensionality and complexity; fluency in SQL (or other database languages) and a scripting language (Python or R)

Responsibilities:

• Develop best practices for instrumentation and experimentation and communicate those to product engineering teams to help us fulfill our mission - to bring everyone the inspiration to create a life they love

• Bring scientific rigor and statistical methods to the challenges of product creation, development and improvement with an appreciation for the behaviors of our Pinners

• Build and prototype analysis pipelines iteratively to provide insights at scale while developing comprehensive knowledge of data structures and metrics, advocating for changes where needed for product development

• Work cross-functionally to build and communicate key insights, and collaborate closely with product managers, engineers, designers, and researchers to help build the next experiences on Pinterest

Benefits:

• Medical, dental, and vision insurance

• 401k plan

• Daily lunch, snacks, and beverages

• Flexible time off

• Competitive salary and equity

Experience Requirements:

• 1+ year of experience directly supporting and executing office management duties.

Responsibilities:

• Greet visitors and oversee daily office operations, ensuring the office environment is clean, welcoming and organized.

• Manage meal deliveries, including coordinating lunch and snack orders, as well as setup and cleanup.

• Handle office maintenance and troubleshooting, liaising with vendors and building management for repairs and replacements.

Experience Requirements:

• 4+ years of experience in Automation Engineering, Integration Engineering, or Business Systems Engineering

Responsibilities:

• Design, build, and maintain automated integrations between business systems (e.g., CRM, billing, support, and marketing tools).

• Create event-driven automation workflows using Python and iPaaS tools (e.g., Workato, Mulesoft, [Tray.io](http://Tray.io), Zapier).

• Streamline key business processes and operational workflows to boost efficiency and reduce manual work.

• Monitor and resolve integration issues while ensuring data integrity across systems. Experience analyzing existing systems and processes to identify integration points and opportunities for improvement.

• Experience with data mapping, cleaning, and validating the data for migration, including ensuring accuracy and compliance with organizational standards.

Experience Requirements:

• 3+ years of experience as a business systems analyst and/or Salesforce support experience

Responsibilities:

• Support the day to day triage of GTM systems from our end users

• Drive end to end project delivery, from requirements gathering through to testing and launch

• Identify areas for GTM systems improvements

Experience Requirements:

• 3+ years of experience as a business systems analyst and/or Marketo support experience

Responsibilities:

• Support the day to day triage of marketing systems from our end users

• Drive end to end project delivery, from requirements gathering through to testing and launch

• Identify areas for marketing systems improvements

Experience Requirements:

• 5+ years of success in your current Customer Success or Account Management role

Responsibilities:

• Drive onboarding, adoption, utilization, and growth in Notion’s existing customer base by owning specific customer accounts

• Proactively identify customers who aren’t maximizing their opportunity with our product/service and reach out to create an action plan to help them achieve success

• Partner with the Account Management team on marquee Mid-Market accounts to expand use cases and create stickier customers

• Learn our product inside and out and help your customers drive business outcomes

Experience Requirements:

• You have 2+ years of experience managing engineering teams with a record of shipping high-quality outcomes in a fast-paced environment.

• You have a sufficiently deep background in the observability, reliability and/or deployment infrastructure.

Responsibilities:

• You'll build and manage a diverse and inclusive team of engineers building deployment infrastructure and observability platform of Notion engineering. You'll create a healthy environment in your team that embodies Notion's values.

• You'll recruit, coach, and develop engineers; you'll ensure engineers are regularly receiving feedback and are making progress on personal and professional goals.

• You'll collaborate closely with engineering teams to provide best in class deployment and observability experience. Your team owns mean time to detect (MTTD) and mean time to recover (MTTR) SLOs for Notion.

Experience Requirements:

• 3+ years

Experience Requirements:

• 3+ years

Experience Requirements:

• 6+ years

Benefits:

• Competitive salary and equity package

• Comprehensive benefits including health, vision, and dental coverage

• High-ownership, high-velocity environment with rapid learning opportunities

• Chance to work on a profound technical problem that, if solved, will accelerate the future of technology development and civilization

Education Requirements:

• A Bachelor's degree in Computer Science, Software Engineering, or related field; advanced degree preferred

Experience Requirements:

• You are an exceptional researcher who has designed large-scale ML systems

• Experience building LLM applications and agentic LLM systems

• Familiarity with cloud computing platforms and distributed systems

• Have strong intuition about computer architecture to quickly understand chip design and verification tasks

• Strong communication abilities and experience working in cross-functional teams

Other Requirements:

• Strong proficiency in Python and AI/ML frameworks like PyTorch

Responsibilities:

• Design and implement core components of our AI-powered engineering systems

• Integrate classical algorithms and deep learning-based approaches across modalities

• Utilize our GPU clusters for efficient training, finetuning, and inference of AI models, ensuring optimal performance and scalability.

• Explore and implement efficient inference techniques to reduce computational costs and improve the speed of model predictions without compromising quality.

• Stay ahead of the curve in AI research, particularly in the areas of LLMs, to continually improve our technologies and methodologies.

Experience Requirements:

• 7+ years of experience in business development, strategic partnerships, or a similar role

• 2 + years of proven experience in building and managing successful partnerships in the Ad-tech industry

Other Requirements:

• Proven experience in high growth companies in the space with demonstrated results accelerating business w/GL/Met - strong advantage

• Strong expertise in performance marketing

• Analytical mindset with the ability to assess and optimize partnership performance.

• Experience working in a fast-paced, high-growth environment.

• Strong network of industry contacts and relationships.

• Ability to think strategically while executing operationally.

• Excellent communication and relationship-building skills

• The position requires 25% travel.

Responsibilities:

• Build, maintain, and grow strategic relationships with partners such as Meta and Google.

• Identify and evaluate potential strategic partners aligned with business goals.

• Develop and execute partnership strategies to drive mutual growth and value.

• Negotiate and manage partnership agreements, ensuring clear terms and successful execution.

Responsibilities:

• Map and define sophisticated enterprise buyer personas across verticals, with specific focus on how different industries evaluate and adopt ML technology

• Track emerging ML model architectures & capabilities to identify strategic market opportunities and inform product positioning

• Transform complex ML infrastructure concepts into compelling, vertical-specific value propositions that resonate with both technical and business stakeholders

• Create technical content and sales enablement materials that demonstrate our unique approach to enterprise ML deployment

• Drive end-to-end launch strategy for new ML capabilities, ensuring GTM teams can effectively position our technical differentiation within each target vertical

Responsibilities:

• Own end-to-end customer relationships from technical discovery through implementation and success validation

• Translate customer use cases into detailed technical implementation plans for the Applied ML Engineering team

• Conduct technical discovery sessions with customers to deeply understand their requirements, constraints, and success criteria

• Build and maintain the technical roadmap for each customer engagement, identifying risks and dependencies early

• Serve as the escalation point for complex technical challenges encountered during implementations

Responsibilities:

• Create and maintain data cleaning, filtering, selection pipeline than can handle >100TB of data.

• Watch out for the release of public dataset on huggingface and other platforms.

• Create crawlers to gather datasets from the web where public data is lacking.

• Write and maintain synthetic data generation pipelines.

• Run ablations to assess new dataset and judging pipelines.

Responsibilities:

• Design and implement high-performance, scalable training infrastructure that efficiently utilizes our GPU clusters for both specialized and large-scale multimodal models

• Build robust data loading systems that eliminate I/O bottlenecks and enable training on diverse multimodal datasets

• Develop sophisticated checkpointing mechanisms that balance memory constraints with recovery needs across different model scales

• Optimize communication patterns between nodes to minimize the overhead of distributed training for long-running experiments

• Collaborate with ML engineers to implement new model architectures and training algorithms at scale