Software Engineer - Machine Learning Infrastructure

• Demand for online food delivery is growing really fast! In the last 5 years, just in the US, the overall market has expanded 10X from $10B to $100B, and could expand to $500bn- $1T by 2030.
• Changing the restaurant industry: You'll be part of a team that helps restaurants succeed in online food delivery.
• Collaborative environment: You will receive support and guidance from experienced colleagues and managers, helping you to learn, grow and achieve your goals, and you'll work closely with other teams to ensure our customer's success.

What else you need to know

This role is based in our Mountain View or San Francisco office location. As a company driven by innovation and continuous change, close collaboration is essential. We're constantly reimagining our industry, creating new products, and refining our processes, and we do our best work together. That's why all of our office-based teams work onsite, five days a week.

• 6+ years of experience as a software engineer, and the ability to work independently in a fast-paced, dynamic environment, taking initiative to drive results.
• Background in data infrastructure, platform infrastructure or ML infrastructure is preferred.
• Enthusiastic about learning new codebases, and making changes in large complex systems safely and efficiently.
• Proficiency in Python and Java programming languages. We're looking for engineers who are comfortable with multiple languages and have experience building scalable high-performance systems
• Familiarity with Azure and GCP cloud environments, and deployment tools such as Docker and Kubernetes
• Experience in real estate, food technology, or related industries is a bonus, though not required. A passion for solving complex problems in these sectors is key., How many years of relevant work experience do you have for this position? *

The base salary range for this role is $176,000 - 230,000 per year.

Actual compensation will be determined on an individual basis and may vary depending on experience, skills, and qualifications.

Base salary is just one part of your total rewards package. You may also be eligible for equity awards and an annual performance-based bonus.

Benefits Summary (USA Full-Time Exempt Employees):

• Medical, dental, and vision insurance (multiple plans, incl. HSA options).

• Company-paid life and disability insurance (short- and long-term).

• Voluntary insurance: accident, critical illness, hospital indemnity.

• Optional supplemental life insurance for self, spouse, and children.

• Pet insurance discount.

• 401(k).

• Health Savings Account (HSA)

• Flexible Spending Accounts (Healthcare, Dependent Care, Commuter)

• Time Off policies:

• Discretionary vacation days

• 8 paid holidays per year

• Paid sick time

• Paid Bereavement leave

• Paid Parental Leave, As set forth in CloudKitchens's Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law. Gender Are you Hispanic/Latino? Please identify your race Race & Ethnicity Definitions

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CloudKitchens helps restaurateurs around the world succeed in online food delivery - our goal is to make food more affordable, higher quality and convenient for everyone. We take underutilized properties and transform them into smart kitchens so they can better serve restaurateurs, customers and the neighborhoods they're in. Every time we launch a new facility we create jobs in that neighborhood, and we're proud to provide a wide range of cuisines and options for healthy food at an affordable price. We're changing the game for restaurateurs whether they're entrepreneurs opening their first restaurant all the way through to your favorite global quick-service restaurant chains. What you'll do * Work on the Machine Learning and AI Infrastructure team, which is focused on providing infrastructure for data scientists and other engineers to use ML and AI technologies effectively. * Build and maintain software and tools for classical ML stack which helps data scientists manage full training lifecycle from data collection, data preparation, training, deployment and model serving. * Build and maintain software and tools which enable efficient and effective use of GenAI (LLM) technologies.