Staff Engineer Onboarding Plan

Here's an onboarding plan tailored specifically for a Staff Engineer in Applied Science team:

First Week: Orientation and Understanding the Landscape

1. Company & Team Orientation:

   • Attend company-wide onboarding sessions to understand the overall mission, values, and culture.

   • Meet with HR to review any specific policies and benefits that apply to your role.

2. Team Introduction:

   • Set up 1:1 meetings with key members of the Applied Science team, including Data Scientists, ML Engineers, and Product Managers.

   • Understand the structure and objectives of the Applied Science team.

3. Access to Tools & Infrastructure:

   • Ensure access to all necessary tools, including code repositories, data pipelines, machine learning platforms, cloud services, and CI/CD systems.

   • Familiarize yourself with the team's tech stack (ML frameworks, data platforms, etc.).

4. Shadow Senior Engineers/Scientists:

   • Pair up with senior engineers and scientists to observe the workflows, tooling, and code review processes.

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First Month: Deep Dive into Science & Technology

1. Get Acquainted with Applied Science Objectives:

   • Study the existing machine learning models, algorithms, and systems the team has built.

   • Review research papers, documentation, and the current state of experimentation.

2. Data Ecosystem Understanding:

   • Learn about data sources, data infrastructure, and the processes for data collection, cleaning, and usage in the Applied Science context.

   • Participate in data engineering meetings to better understand how data flows through the system.

3. Early Contributions:

   • Begin contributing to ongoing projects by reviewing code or offering insights on model development and optimization.

   • Assist with tuning, debugging, or refactoring codebases to improve model performance or data pipeline reliability.

4. Collaboration & Communication:

   • Start attending regular stand-ups, design reviews, and brainstorming sessions with the Data Science and Engineering teams.

   • Schedule regular check-ins with your manager to ensure alignment with goals and performance expectations.

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First 3 Months: Leadership in Applied Science

1. Own a Project or Initiative:

   • Identify a key area in the Applied Science space (e.g., model optimization, new feature development, or an infrastructure improvement) that you can lead.

   • Collaborate with the team to deliver improvements to machine learning pipelines, data engineering frameworks, or experiments.

2. Cross-Functional Engagement:

   • Build strong relationships with other teams like Product, Data Engineering, and Infrastructure to ensure alignment on larger technical strategies.

   • Share your insights on the state of machine learning infrastructure and suggest potential optimizations.

3. Mentor & Support Team Members:

   • Provide mentorship to junior engineers or data scientists, helping them improve their technical skills and guiding them on best practices.

   • Participate actively in code and model reviews, bringing your engineering expertise to improve the robustness of solutions.

4. Experimentation & Innovation:

   • Lead or support innovation efforts, such as experimenting with cutting-edge ML techniques or designing new algorithms to solve specific business challenges.

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6 Months: Strategic Leadership & Long-Term Contribution

1. Drive Strategic Applied Science Initiatives:

   • Lead larger projects or initiatives aimed at improving the scalability and efficiency of machine learning models or data systems.

   • Propose new research areas or product features based on data insights and scientific experimentation.

2. Scale Your Impact:

   • Engage in broader company-wide discussions about machine learning, AI ethics, data governance, and infrastructure at scale.

   • Work with other senior leaders to ensure Applied Science team stays aligned with long-term goals and delivers impactful results.

3. Measure & Communicate Impact:

   • Track and report the impact of your work, highlighting improvements in model accuracy, efficiency, or data handling.

   • Start influencing larger company initiatives by contributing to architecture reviews, product roadmaps, or strategic discussions around data-driven products.

4. Long-Term Planning:

   • Align your personal goals with broader vision, collaborating with leadership on how you can drive value over the next 12 months and beyond.

   • Focus on building systems that will help scale Applied Science efforts across the company.

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This onboarding plan will help you adapt to the new environment and make impactful contributions. It’s important to stay flexible and adjust your plan as you get more familiar with the team and company priorities.