IC2 Machine Learning Engineer
I am prolific at delivering resilient and sustainable software projects from design to implementation and rollout
Area of ownership and level of autonomy / ambiguity
Organizational reach and extent of influence
Technical levers typically exercised to achieve business impact
- I execute on defined projects to achieve team-level goals.
- I independently define the right solutions or use |existing approaches to solve defined problems.
- I work primarily within the scope of my team with high level guidance from my manager/TL
- Craft - I am increasingly mastering my craft and leverage it for higher impact (e.g. software design)
- Mentorship - I may mentor new hires, interns, or more junior engineers.
- I act with urgency and deliver high-quality work that will add the most value
- I work with my manager to direct my focus so my work advances my team's goals
- I prioritize the right things and don't overcomplicate my work. When necessary, I propose appropriate scope adjustments.
- I effectively participate in the core processes of my team, including recommending and implementing process improvements
- I follow through on my commitments, take responsibility for my work, and deliver on time
- I proactively identify and advocate for opportunities to improve the current state of projects
- I own my failures and learn from them
- I think a step or two ahead in my work, solve the right problems before they become bigger problems, and problem-solve with my manager when I'm stuck
- I Identify and gather input from others and consider customer needs to make informed and timely decisions
- I’m open to change and enthusiastic about new initiatives
- I work with my manager to navigate complex and ambiguous situations
- I ask questions and contribute to new ideas/approaches
- I experiment with new approaches and share what I learned
- I proactively ask for feedback from those I work with and identify ways to act upon it
- I have self-awareness about my strengths and areas for development
- I drive discussions with my manager about aspirational goals and seek out opportunities to learn and grow
- I contribute to interviewing and assessing candidates to help us build a diverse and talented team. I am calibrated and consistently perform high-signal interviews
- I am able to represent my team’s initiatives and goals to candidates in a compelling way
- I model integrity and a high standard of excellence for my work.
- I help the more junior members of my team, host interns, or am a residency mentor
- I offer honest feedback that is delivered with empathy to help others learn and grow
- I can effectively collaborate to get work done
- I work with my manager to manage conflict with empathy and cooperation in mind
- I contribute to a positive sense of community on the team (e.g. engage in team lunches, team offsites, and other group activities, help with new-hire on-boarding).
- I listen to different perspectives and I cut biases from my words and actions
- I practice the Dropbox Diversity Commitments on a regular basis
- I write and speak clearly
- I listen to understand others and ask clarifying questions
- I share relevant information on my projects to my manager, team and customers.
I focus on effectively applying standard tools to build high-quality models, and learning to deliver software projects using ML. I understand the ML development lifecycle* for my projects. I can consistently deliver high-quality code when needed.
- I work effectively with the ML tools and software packages used by my team.
- I understand the ML algorithms and techniques used in my area, and can adapt them to my project as needed.
- I can analyze and present datasets or results of experiments with statistical methods and/or visualization techniques my team may specify for me.
- I build a model from standard components in order to solve a given computational task.
- I understand the stages of ML development lifecycle* and their interactions within my projects, and make adjustment to existing designs for any stage when necessary.
- I understand the reasoning behind my team’s design decisions in order to implement the designs.
- I translate ideas into clear code, written to be read as well as executed
- My code is free of glaring errors - bugs are in edge cases or design, not mainline paths - and is well documented and well tested with appropriate use of manual vs automated tests
- I’m able to read and navigate through a large code base and effectively debug others’ code
- I address code tasks with both high throughput and appropriately high quality for the stage of project I am working on
* ML development lifecycle refers to the stages/tasks in the life of an ML projects: task formulation; dataset collection, cleaning, and aggregation; feature extraction; modeling, optimization, and evaluation; off/on-line testing, deployment and monitoring; and iteration based on feedback from each stage.