Senior Machine Learning Engineer

Why CAST AI?

CAST AI is the leading Kubernetes cost optimization platform for AWS, GCP and Azure customers. The company is on a mission to deliver a fully automated Kubernetes experience. What’s unique about CAST AI is that its platform goes beyond monitoring clusters and making recommendations; it utilizes advanced machine learning algorithms to analyze and automatically optimize clusters, saving customers 50% or more on their cloud spend, improving performance and reliability, and boosting DevOps and engineering productivity.

The company has raised $73M from investors, including Cota Capital, Creandum, Uncorrelated Ventures, and Vintage Investment Partners. CAST AI has more than 120 employees globally and is headquartered in Miami, Florida.

However, this is merely the beginning. Our product roadmap is filled with exciting innovations that are yet to come. We are searching for intelligent, motivated, and self-reliant people to help us fulfill this ambitious mission.

Core values that hold us all together:

PRACTICE CUSTOMER OBSESSION. Focus on the customer journey and work backwards. Strive to deliver customer value and continuously solve customer problems. Listen to customer feedback, act, and iterate to improve customer experience.

LEAD. Take ownership and lead through action. Think and act on behalf of the entire company to build long-term value across team boundaries.

DEVELOP AND HIRE THE BEST. Strive to raise the performance bar by continuously investing in yourself, the team and by hiring the best possible candidates for every position. Drive towards personal development and professional growth, and mentor others to raise the collective bar.

EXPECT AND ADVOCATE CHANGE. Strive to innovate and accept the inevitable change that comes with innovation. Constantly welcome new ideas and opinions. Share insights responsibly with unwavering openness, honesty, and respect. Once a path is chosen, be ready to disagree and commit to a direction.

---

Role overview

We are looking for a Senior Machine Learning Engineer who will play a pivotal role in harnessing the power of data to drive operational excellence. You will design, develop and deploy sophisticated data models, leverage cloud-native technologies, and contribute to our DevOps and Machine Learning operations.

Responsibilities

• Develop and maintain ML training, validation, and deployment pipelines
• Utilize cloud-native technologies to optimize data workflows and ensure seamless integration with our existing platforms
• Participate in cross-functional projects and collaborate with various teams to achieve company goals
• Collaborate with data scientists to streamline model hand-off and production readiness.
• Stay current with the latest industry trends and advancements in data science, cloud technologies, and DevOps practices
• Ensure data compliance and security measures are upheld across all operations.

---

Requirements

• Proven experience in applied Data Science/Machine Learning, with a strong portfolio of projects that demonstrate your expertise
• Strong programming skills in Python (proven experience with pandas, sklearn, pytorch would be a great plus) and SQL
• Proficiency in cloud-native technologies and understanding of cloud architecture (AWS, GCP, Azure, or similar)
• Solid understanding of DevOps practices, with experience in CI/CD, infrastructure as code, containerization, and orchestration (Docker, Kubernetes, or similar)
• Familiarity with ML pipeline tools and practices, including data collection, preprocessing, model training, deployment, and monitoring
• Excellent problem-solving abilities and attention to detail
• Strong communication skills and the ability to work effectively in a team-oriented setting.

Bonus points

• Experience with MLOps tools like MLflow, Feast, Kubeflow
• Experience with large-scale data processing tools like Spark, Ray, Apache Beam/Flink
• Experience in training time-series and deep learning models.

---

What’s in it for you?

• Team of highly skilled professionals to work with and learn from

• Impact and visibility. Our organization is flat, getting in touch with CEO or CTO is a common practice here

• Short feedback loop. We have an obsession with customer satisfaction. The ship features fast and gets instant feedback. Feature projects tend to be completed in 1 to 4 weeks, depending on the scope

• Flexible working hours. We deliver instead of sitting in the office 8 to 5

• Skin in the game. Every employee gets a share of the company

• Time to focus on work with a minimum overhead of meetings, bureaucracy, etc.

• 10% time to focus on self-improvement or personal projects.