Why Cast AI?

Cast AI is an automation platform that operates cloud-native and AI infrastructure at scale. By embedding autonomous decision-making directly into Kubernetes and cloud environments, Cast AI continuously optimizes performance, reliability, and efficiency in production.
The old way doesn't work. As Kubernetes and AI environments grow, manual decisions don’t. Cast AI replaces tickets, alerts, and manual tuning with continuous automation that adapts infrastructure as conditions change. Efficiency and cost savings follow naturally from that automation.
Over 2,100 companies already rely on Cast AI, including Akamai, BMW, Cisco, FICO, HuggingFace, NielsenIQ, Swisscom, and TGS.

Global team, diverse perspectives

We're headquartered in Miami, but our impact is international. We take a global and intentional approach to diversity. Today, Cast AI operates across 34 countries spanning Europe, North America, Latin America, and APAC, bringing a wide range of perspectives into how we build and lead.

Unicorn momentum

In January 2026, we achieved unicorn status with a strategic investment from Pacific Alliance Ventures, the corporate venture arm of Shinsegae Group (a $50+ billion Korean conglomerate). Our valuation now exceeds $1 billion, and we're just getting started.

Join us as we build the future of autonomous infrastructure.

About the role

In the AI Enabler team, our day is usually full of R&D challenges. Have you ever encountered a situation where you need to expand your AI infrastructure so that the applications can automatically pick the right large language models (LLMs) that are both more cost-efficient and better performing? Most of us probably do nowadays, or at least understand the complexity of making such decisions while keeping track of our cloud budget.

One of the team's responsibilities is ensuring that whenever a customer makes AI-related decisions regarding their K8s infrastructure, they are implemented automatically without unnecessary costs or hassle. This is just one small piece of a bigger puzzle. To get into a more detailed perspective, ask yourself the following questions:

How often do you use LLMs?
What is the least expensive LLM you can pick for a given prompt without degrading the quality of the response?
How much do your applications cost per 1 million tokens and how can you improve it?
Which API keys have the biggest waste?
How can you improve your frequently running prompt to use fewer tokens?
What is fine-tuning and how to do it efficiently?
What is a transformer?

These are just several of the many questions that are part of the daily work of this team.

Being a part of this team would involve design and decision-making end-to-end while collaborating with colleagues from other teams. Cast AI, being a technical product, encourages not only coding something as written in the JIRA ticket but also coming up with new features and potential solutions to customers' problems. Given that the team is working on a technical greenfield project, you will have the opportunity to impact it in many ways positively.

Here are some of the tools we use daily:

Python
vLLM, SGLang, TensorRT, PyTorch
ClickHouse and PostgreSQL for persistence
GCP Pub/Sub for messaging
gRPC for internal communication
REST for public APIs
Kubernetes, which our product is evolving around
AWS, GCP, and Azure cloud providers, which are currently supported in our platform
We use GitLab CI with ArgoCD as our GitOps CD engine
Prometheus, Grafana, Loki, and Tempo for observability.

Requirements:

5+ years of hands-on experience in Data Science and Machine Learning, with a proven track record, demonstrated through a robust portfolio of projects.
Strong software engineering skills in Python.
Ability to move fast in an environment where things are sometimes loosely defined and may have competing priorities or deadlines.
Expertise in ML inference optimizations, including techniques such as:
- Reducing initialization time and memory requirements;
- Utilizing reduced precision and weight quantization;
- Inference engine tuning (vLLM, SGLang, TensorRT).
Knowledge of network optimization for distributed ML training and inference.
Understanding of distributed training patterns and checkpointing strategies.
You have to be physically in any of the European countries GMT 0 to GMT +3.
Strong English skills.
Strong verbal and written communication skills.
Ability to work independently and collaborate in a group.

Responsibilities:

Evaluate and Analyze LLM performance.
Architect and build inference and training pipelines, directly contributing through hands-on design, model training pipeline, and deployment strategies.
Stay up to date with industry trends.

What’s in it for you?

Competitive salary (€6,500 - €9,000 gross, depending on the level of experience)
Enjoy a flexible, remote-first global environment.
Collaborate with a global team of cloud experts and innovators, passionate about pushing the boundaries of Kubernetes technology
Equity options.
Private health insurance.
Get quick feedback with a fast-paced workflow. Most feature projects are completed in 1 to 4 weeks.
Spend 10% of your work time on personal projects or self-improvement.
Learning budget for professional and personal development - including access to international conferences and courses that elevate your skills.
Annual hackathon to spark new ideas and strengthen team bonds.
Team-building budget and company events to connect with your colleagues.
Equipment budget to ensure you have everything you need.
Extra days off to help maintain a healthy work-life balance.

Hiring process

Screening call with Recruiter
Hiring Manager interview
Technical interview (system design)
Live coding
Culture Check interview with an executive

*As part of our standard hiring process, we would like to inform you that a background check may be conducted at the final stage of recruitment through our third-party provider, Checkr.
*Please note that Cast AI does not provide any form of visa sponsorship/work permit.

#LI-Remote

Senior Machine Learning Engineer - AI Enabler Team