Engineering Manager - Machine Learning Extended Environment- Cortex
Who We Are:
Machine learning is advancing products at Twitter (e.g., Timeline ranking, On-boarding) and Cortex is advancing Machine learning at Twitter. Cortex is the central ML/AI team with the goal to build an ML platform and provide deep ML expertise to support our internal customers, while advancing ML inside & outside Twitter.
In particular, the ML Extended Environment team (MLX) in Cortex is focused on unifying & advancing recommendation systems. From timeline ranking to ads ranking to new user on-boarding, recommendation systems are prevalent at Twitter. We are building shared components to unify & advance recommendation systems, e.g., embeddings and approximate nearest neighbor solutions.
MLX team has a unique mix of ML engineers & scientists who work together to explore & build new prototypes and scale them to augment Twitter’s ML capability. Finding the right balance between innovation and impact is key to making this team successful. This is an exciting and creative area, with a unique opportunity to push the boundaries of machine learning further.
What You’ll Do:
You will lead diverse, smart and driven ML engineers/scientists and align their career ambitions with business requirements and opportunities. We're looking for a leader who can collaborate with our partners in different product teams. As a manager for MLX you will:
Establish the vision and mission for the MLX team. Be responsible for the team’s technical strategy and roadmap.
Look ahead to identify opportunities and mitigate risks. Be a strategic leader in the Cortex organization.
Be an engineering and research talent magnet to make the team successful in your established mission.
Mentor the professional development of each direct report through personal and performance management.
Give engineers the tools, confidence, and motivation to make decisions independently that lead to the recognition of your engineers and the team.
Communicate expectations, feedback and context to team members, partners and stakeholders early and often, succinctly and with empathy.
Build strong relationships with the customer teams and actively understand & partner with them. Enroll customers into co-developing and adopting platform solutions.
Project examples: Entity co-embeddings, Candidate generation systems, End-to-end relevance engines, Semi-supervised learning
Who You are:
You have experience leading a team of engineers with responsibility for machine learning & software development in production.
You have knowledge of and experience with techniques used in deep learning, recommendation systems, reinforcement learning.
You have a passion for taking customer-driven approach to building software. You take pride in owing & maintaining what you have built.
You are skilled at coaching and guiding your directs through their career development. You believe in building both teams and products that scale.
You are adept at communicating relevant information clearly and concisely.
You’re capable of collaborating with our business, product, engineering, design and research colleagues to discover new opportunities that benefit Twitter.
MS or PhD in Computer Science with a focus on machine learning, mathematics or related quantitative field or commensurate work experience.
Research publications, Technical blog posts or publications, Open source contributor
We are committed to an inclusive and diverse Twitter. Twitter is an equal opportunity employer. We do not discriminate based on race, color, ethnicity, ancestry, national origin, religion, sex, gender, gender identity, gender expression, sexual orientation, age, disability, veteran status, genetic information, marital status or any legally protected status.
San Francisco applicants: Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Engineering Hiring Process
Once your application is received, a recruiter will reach out pending your qualifications are a match for the role.
If your background is a match, you may have 1-2 technical phone interviews or be given the chance to provide a work sample depending on the role.
If the phone interviews go well or your work sample is strong, the final step includes interviews with 5-6 people held onsite in our office.
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