Lead Engineer, Machine Learning

About The Role

Over 5 billion people use basic applications like email, notes, tasks, and calendars that are not AI-native. Our mission is to develop proactive applications accessible to everyone, especially those unfamiliar with complex AI prompts. We strive to infuse intelligence into conversations, errands, organization, and workflows with minimal user input.

Our product emphasizes robust reliability for long-running workflows, persistent context, and real-world task fulfillment, aiming to minimize AI hallucinations. Our ultimate goal is to organize users' lives, helping them focus on meaningful and valuable activities.

As the Lead Engineer in Machine Learning, you will be responsible for implementing the intelligence execution layer by converting research and model capabilities into dependable and scalable production systems. This role covers the full model lifecycle, including data management, training, evaluation, inference, and deployment. It is a hands-on leadership position working at the nexus of research, systems, and product development.

Responsibilities

• Manage and lead the end-to-end machine learning systems powering the company, spanning data collection, training, evaluation, inference, and deployment.
• Develop and maintain pipelines for training and fine-tuning large-scale models.
• Create evaluation frameworks to assess model capabilities, robustness, safety, and real-world application performance.
• Design high-performance inference infrastructure optimizing latency, GPU usage, memory footprint, operational cost, and system reliability.
• Construct data pipelines and systems to generate and handle high-quality synthetic and real-world training data.
• Establish reliable production environments for deployment, monitoring, and continuous model improvement.
• Collaborate closely with research and application engineering teams to translate model capabilities into enhanced product features.
• Make pragmatic technical decisions and rapidly iterate based on measured real-world outcomes.

Requirements

• Proven experience delivering and operating machine learning systems in production environments, beyond just research prototypes.
• Strong grasp of modern techniques in large model training, fine-tuning, evaluation, and inference optimization.
• Solid foundation in software engineering principles and systems architecture.
• Experience managing machine learning workloads at scale, particularly with GPU-powered systems.
• Excellent technical judgment and capacity to independently handle ambiguous challenges.
• A proactive approach toward experimentation, measurement, and delivering shipped solutions.
• Commitment to high standards of correctness, dependability, and production quality.

Outcomes Expected

• Seamless translation of research and models into production-ready applications with defined performance and quality criteria.
• Stable, efficient, and maintainable ML pipelines, training loops, and inference systems.
• Swift detection, diagnosis, and resolution of production issues to reduce impact on users.
• Supportive team environment enabling members to contribute impactful ML work with minimal obstacles.
• Regular, safe iterations on models and systems that measurably enhance user experience.

Technical Stack

• Python programming language
• PyTorch and JAX frameworks
• GPU-based training and inference systems

Ideal Profile

• History of developing or deploying machine learning systems utilized in real-world applications rather than demonstrations.
• Comfortable working with large models and diagnosing their failure scenarios.
• Produces robust, production-quality code with strong emphasis on system correctness.

Work Environment

We are a compact, highly skilled, hands-on team where engineers exercise broad ownership and independent judgment. We value swift decision-making, close collaboration, and balancing speed with engineering fundamentals. Our focus is less on processes and more on creating exceptional products.

Interview Process

Candidates who meet the requirements will be invited for 3 to 4 interviews, conducted virtually and/or onsite. The evaluation is performed by technical team members, and decisions are communicated promptly. Successful candidates will receive an offer to join a team dedicated to delivering practical AI benefits to billions worldwide.