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

Throughput. Latency. KV cache utilization.

Move those three numbers in the right direction, and two things happen: customers get faster, cheaper inference, and our margins improve. That's the entire thesis of this role. Every kernel you tune, every quantization scheme you ship, every scheduler tweak you land shows up directly in a customer's p99 and on our P&L.
This is a high-impact seat. It is also a high-autonomy seat as you'll be given the room to lead the technical direction of inference optimization at Kimchi, not execute someone else's roadmap.

The problem: running LLMs in production is a moving target. The "right" model and serving configuration for a workload depend on traffic shape, sequence-length distribution, batch dynamics, GPU SKU, memory bandwidth, quantization tolerance, and a dozen other variables that shift week to week. Most teams pick a model once, over-provision GPUs, and absorb the cost. Kimchi is the system that makes that decision automatically - continuously matching workloads to the most cost-efficient, best-performing LLM and serving configuration on a customer's infrastructure. We're building the optimization layer between the model and the hardware, and we need engineers who understand both sides deeply.

Stack

Python; vLLM; SGLang; TensorRT-LLM; PyTorch; CUDA-adjacent tooling; Kubernetes; gRP; ClickHouse; PostgreSQL; GCP Pub/Sub; AWS / GCP / Azure; GitLab CI; ArgoCD; Prometheus; Grafana; Loki; Tempo.

Requirements:

• 5+ years building real ML systems, with a portfolio that shows depth in inference or training infrastructure (not just model training notebooks).
• Strong Python - production services, not scripts.
• Hands-on experience with at least one of vLLM, SGLang, or TensorRT-LLM, and a working mental model of why an inference engine performs the way it does on a given GPU.
• Fluency with quantization tradeoffs - you've measured quality regressions, not just compression ratios.
• Comfort with distributed systems: collective communication, sharding strategies, and the practical failure modes of multi-GPU and multi-node setups.
• A bias toward measurement. You instrument before you optimize, and you can tell the difference between a real win and a benchmark artifact.
• Self-direction. This role comes with a wide mandate; you should be excited by that, not unsettled by it.

Responsibilities:

• Push throughput. Continuous batching, speculative decoding, chunked prefill, kernel-level tuning across vLLM, SGLang, and TensorRT-LLM. Find the ceiling on each GPU SKU, then raise it.
• Cut latency. Attack TTFT and TPOT separately. Profile, identify the actual bottleneck (compute, memory bandwidth, scheduling, networking), and fix it - not the bottleneck you assumed.
• Get more out of the KV cache. Paged attention, prefix caching, eviction policies, cache reuse across requests, quantized KV. This is where a lot of the unrealized throughput lives, and it's an area you'll own.
• Quantize without regressing quality. INT8, INT4, FP8 across weights, activations, and KV. Empirical work: measure quality on real workloads, not just perplexity benchmarks.
• Shrink cold starts and memory footprint. Faster init, smarter weight loading, tighter memory accounting - the difference between a model that scales and one that doesn't.
• Scale across nodes. Distributed inference topologies, network-aware placement, checkpointing strategies that don't bottleneck on storage or interconnect.
• Set the technical direction. Decide what we benchmark, what we adopt, and what we build ourselves. Bring the team along with strong writeups and reproducible experiments.

What’s in it for you?

• Competitive salary (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.
• 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

Your mission

Join Bitpanda as a Senior DevOps Engineer and take a leading role in shaping the next generation of our high-performance, cloud-native infrastructure that powers the future of digital finance. As a senior member of the team, you will drive architectural decisions, mentor and guide junior engineers, and ensure our platforms remain secure, scalable, and resilient. You’ll work with a motivated group of highly skilled engineers, contributing your experience while continuing to grow in a cutting-edge, fast-moving environment.

What You’ll Do

• Design, develop, and evolve our AWS-based infrastructure using best-in-class tooling, including Terraform/CloudFormation and ArgoCD, leading the operation and optimisation of our container orchestration platforms, with a focus on multi-cluster networking and service-to-service communication.
• Take ownership of our cross-cluster Istio service mesh—implementing policies, improving traffic management, and enhancing mesh-level networking performance and security
• Monitor, optimise, and automate systems to ensure high availability, strong security posture, and superior performance using Datadog, Opsgenie, and AI-powered tooling
• Build scalable, robust solutions for complex technical challenges, ensuring infrastructure design aligns with best practices and long-term platform goals.
• Mentor junior and mid-level engineers, promote engineering excellence, and contribute to team knowledge sharing and process improvements.

Who You Are

• 5+ years of hands-on experience building and managing AWS infrastructure (EC2, VPC, IAM, RDS, KMS, and related services).
• Strong expertise in Docker, Kubernetes, and Infrastructure as Code (Terraform/CloudFormation).
• Advanced understanding of cloud networking, including routing, load balancing, VPC design, hybrid connectivity, and service-mesh-based networking - familiarity with multi-cluster service mesh (Istio) and related capabilities is preferred
• Proficiency in CI/CD systems, with deep experience in GitLab CI/CD and working knowledge of ArgoCD.
• Proven experience deploying, maintaining, and scaling critical cloud infrastructure components in production environments.

** We offer a comprehensive relocation package to support candidates who are moving to join our team. Our goal is to make your transition as smooth as possible, and we provide assistance throughout the whole process.

The position

You'll be joining a team of engineers across Frontend, Backend, SRE and QA. We're organised into cross-functional development teams assigned to specific verticals.

This role is open for several teams, and we will define the exact team that you will be joining during the interview process based on the business needs and your preferences.

Regardless of the specific team, you will be working on building tools, APIs and integrations for one of our products.

Our backend is built with Elixir and Phoenix, with a Postgres database. We use React and Nextjs for our front-end. Gitlab is used as a version control tool, issue tracker and a CI/CD solution. Our applications are hosted on AWS. We fully rely on our CI for deployments and deploy multiple times per day.

What this job can offer you

• Complex and meaningful challenges — solving them will enable people and businesses to live and operate in any country of the world.
• Opportunity to have a significant impact on the business — we are still very early in our journey as a company, and each change you make today is amplified by the company's growth.
• A lot of freedom to organize your work and life — you are not bound to daily standups, recurring meetings or other ceremonies.
• Competitive salary, stock options, flexible paid time off, and a set of perks and benefits.
• A supportive and kind work environment where we would like you to challenge the dogmas and pursue innovation!
• Strong team of experienced engineers who will support and facilitate your professional growth.

What you bring

Must have (professional experience):

• Strong engineering fundamentals and a track record of shipping production systems that are secure, reliable, and scalable.
• Practical experience designing or adopting agentic/automation workflows (or comparable systems) and improving them through iteration.
• Ability to think in systems: define specs clearly, break down plans, instrument verification, and close the loop on quality.
• Postgres (or similar).
• CI/CD (GitLab, Github, Jenkins or similar).

Nice to have

• Kubernetes
• Docker
• AWS
• Nextjs
• React/Vue/Angular

Key Responsibilities

• Lead the development of major team-scoped projects, participate in cross-team initiatives for Remote's HR and Payroll products.
• Actively participate in product work in the team: provide feedback, suggest solutions to the problems. Use technical insights and expertise to suggest product improvements.
• Maintain good understanding of the team’s domain, both from product and engineering sides.
• Provide feedback on code reviews.
• Contribute to the shared codebase.
• Debug and solve technical and business issues.
• Participate in non-team activities, such as support rotations, hiring process, RFC discussions, etc.
• Mentor and provide guidance to other engineers.
• Investigate, propose and participate in implementation of improvements to our platform.
• Implement interfaces with performance, accessibility, and API design in mind.
• Redesign how engineering work ships with autonomous agents as the default execution layer
• Propose and operationalize agentic workflows end-to-end (spec → plan → execute → verify) to deliver outcomes faster.
• Build reusable agentic workflows and primitives in the codebase so teams can apply them repeatedly across domains.
• Use verification loops (tests, checks, evals, guardrails) to ensure results are correct, secure, reliable, and scalable.

Practicals

• Reporting line: Engineering Team Leader
• Team: Engineering
• Location: Anywhere in the World
• Start date: As soon as possible

Application process

1. Interview with our recruiter
2. Interview with an Engineering Leader
3. (async) Code exercise and review
4. Interview with members of the engineering team
5. Bar Raiser Interview
6. Executive Interview with VP of Engineering
7. Offer and Prior employment verification check

Job Title: Hardware Design Engineer

Reports to: Director of Hardware Engineering

Department: Engineering

💼 About the role

We’re seeking an experienced hardware design engineer to expand our cryptographic hardware IP team. You would be joining a mature design team with a proven track record of delivering market leading cryptographic IP, your role would expose you to the latest cryptographic standards and allow you to develop for the latest technologies.

We hope that you have at least 5 years of experience working on hardware design of complex IPs targeted at deployment on latest generation FPGAs, preferably with some experience working with cryptographic and security IPs.

 We have been established since 2018, so you would be directly working with the founders and inventors of this deep tech startup. The technology is already a couple of years in the making, its feasibility having been established, funding secured, and it has a clear path to market. This is a great opportunity for career progression and to get a head start in a rapidly expanding segment of the technology industry.

⚒️ What you’ll be doing

• Design and implementation of hardware IP blocks and subsystems.
• Tailoring implementations to FPGAs
• Working with verification engineers to create verification and test plans.
• Creation of reliable, reusable and excellently structured hardware design flows.
• Providing feedback to architects on feasibility of implementation and specification quality.
• Ensuring clean lint and implementation reports for IP blocks and subsystems.

🎯 Required Skills And Qualifications

• A commitment to high quality engineering in all areas of their work, including advocating for the best long-term solutions over the easiest or quickest.
• Optimising designs for both area and performance in FPGA technologies.  
• Experience building hardware IP blocks from scratch, and owning the process from initial specification through to customer delivery.
• Confidence bringing up a design using a basic “designers testbench”.
• Experience with standard commercial EDA Design tooling flows.
• Knowledge of common bus protocols (e.g. AMBA AXI4, AHB).
• Experience building and simulating hardware/software co-designs.
• Confidence in debugging RTL simulations issues.
• Degree or equivalent in Electronics/Computer Science or other related discipline.

🖥️ Specific Technologies You Are Confident Using

• FPGA and emulation experience. We use Xilinx FPGAs extensively for prototyping and hardware/firmware co-development.
• Logical IP design using SystemVerilog.
• Version control using Git.
• Writing System and subsystem level tests in C, using CMake as a build system.
• Confidence using tools like Bash, TCL, CMake and Makefiles safely and sensibly.

👍 Preferred Skills And Qualifications

• Python scripting for flows and automation 
• Experience debugging gate level simulations.
• Knowledge of Industry standard SoC Security Architectures
• Knowledge of tamper resistance and/or power side-channel security countermeasures.
• Experience using Gitlab CI, or otherwise principles of continuous integration and testing.

🏆 Some of the perks of working with us:

• Unparalleled opportunities to learn and accelerated career development.
• A collaborative, team environment with people who truly love what they do.
• Competitive salary and share option scheme.
• Flexible and hybrid working, and a working from home budget.
• Private health insurance for yourself and your family.
• 25 days annual leave (plus bank holidays and two wellness weeks).
• 50% gym membership discount.
• The chance to work with a spirited, smart, and friendly team!

Interested? Apply today to be part of the future of secure hardware! 🚀