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 · gRPC · 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

The Position

A Team Leader is responsible for all the work of a team and the spokesperson for everyone in it. They are expected to manage the team dynamics and always bring the best in everyone. A team leader is not expected to be the best in a team but rather help others become the best version there is and gear the team towards optimised execution.

Key Responsibilities

• Managing a small product team of 3-5 engineers (backend and frontend).
• 30% of the time spent doing management work. Outside of that actively participates in product development in the their team.
• Being involved in hiring processes for their own team and potentially help out their peers.
• In addition to being involved in hiring, also being responsible for the onboarding process of new engineers.
• By being the spokesperson for everyone in their team they are expected to manage the team dynamics and bring out the best in them.
• Giving continuous and constructive feedback on all the things.
• Helping the team to grow continuously as a group and is responsible for each direct report’s career.
• Providing guidance and coaching to the team.
• 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.

Requirements

• Managed an engineering team of at least 2 people.
• Extensive experience with Backend (Elixir/Phoenix or Ruby or functional programming).
• Commitment to promoting all of Remote's values within the team and the greater organisation.
• 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.
• Solid communication skills.
• Solid knowledge of engineering and development principles and best practices.
• Proven organizational skills with attention to detail.
• Focused on efficient processes with a kind, people-first approach.
• Being able to inspire others and instill confidence.
• Good understand of team dynamics.
• Ambitious and intentional about diversity, equality and inclusion.

Practicals

• You'll report to: Engineering Manager
• Direct reports: 2-5 Engineers
• Team: Engineering
• Location: Anywhere in the world
• Start date: As soon as possible

Application process

Roughly 6 hours across 6 weeks

1. Interview with recruiter
2. Interview with Hiring Manager
3. (async) Code challenge + review
4. Interview with team members
5. Bar Raiser Interview
6. Executive Interview
7. Offer and Prior employment verification check (Read more at remote.com/employment-checks)