Machine Learning Engineer

Description
Supplier Call notes
Sr. ML Engineer - AI Space
Posting new job description.
Additional Role
- Same requirements as the previous opening.
- Additional projects and initiatives driving the need.
- Recently onboarded a person for a previous role but have not filled all of the openings yet.
Top 3-5 Skills
- Strong engineering foundation.
- Ability to understand the problem.
- Ability to approach problems systematically.
- Ability to learn new technologies on the job.
- Some experience with ML technologies, particularly from an engineering perspective.
- Will be using existing models to create software services, not writing research papers.
- Cloud engineering experience is required.
- Thinks systematically.
- Open to remote, hybrid, or onsite candidates.
Hiring Process
- Target start dates are still being discussed.
- Interviews will begin next week and continue on an ongoing basis.
- Initial phone screen with Vladimir.
- Followed by interviews with technical members of the team.
- Decision made after the interview process.
Ideal Profile
- Looking for candidates from strong technology companies.
- Example profile: graduate from UCLA with 2-3 years at Snapchat in the ML domain, or experience with Walmart eCommerce.
- Will begin removing profiles from consideration today.
- Technical understanding
Experience building ML systems such as:
- ML pipelines
- Real-time inference services
- Working with and maintaining feature stores
- Strong foundation in AWS cloud infrastructure.
- Top priority is a solid engineering experience.
- Seems to value candidates with a strong college history and the ability to clearly describe their experience and technical foundation.
Culture
- Each person is assigned to a specific project and focuses on one task at a time.
- Agile software development environment.
- Daily stand-ups to exchange updates and discuss progress.
Onboarding
- New hires receive an onboarding buddy who helps show them the ropes.
- Ramp-up period is typically 1-3 weeks.
- After onboarding, they begin receiving small, simple tasks to continue ramping up.
- By the second month, they should be able to deliver practical, smaller tasks independently.
- By 90 days, they should be able to take responsibility for a specific area of

Description:

PURPOSE:

At Hyatt, we're working to Advance Care through data-driven decisions and automation. This mission serves as the foundation for every decision as we create the future of travel. We can't do that without the best talent - talent that is innovative, curious, and driven to create exceptional experiences for our guests, customers, owners and colleagues.
Hyatt seeks an extraordinary Machine Learning Engineer to help build the algorithmic assets and features that Hyatt guests, members, customers and internal users leverage to transform the guest experience and drive efficiencies across the operations of our business.
In this role you will design and implement algorithmic product architectures to bring our machine learning models to life across the full lifecycle of the product including data ingestion, ML processing, and results delivery/activation. This role will work cross-functionally with various data science teams, data engineering teams, and data architecture teams. The ideal candidate can serve as both solutions architect as well as hands-on implementation engineer and guide the team towards best-in-class algorithmic product implementations.
You will be a part of a ground-floor, hands-on, highly visible team which is positioned for growth and is highly collaborative and passionate about data science.
Applying the latest techniques and approaches across the domains of data science, machine learning, and AI isn't just a nice to have, it's a must.

POSITION RESPONSIBILITIES:

Partner with data scientists to design workflows/architectures that activate ML models and maximize their impact, such as real-time streaming use-cases and offline batch optimizations.
Partner with data scientists to develop prototype solutions of algorithmic products leveraging appropriate AWS services with appropriate consideration for scale and latency where applicable.
Implement and product ionize final solutions via…