Platform Engineer - Self Hosting LLMs built GO

PhysicsX is the physics AI company for industrials. The company’s mission is to accelerate hardware innovation by overhauling what industrial engineering and manufacturing look like today. PhysicsX is building a new simulation software stack to deliver deep physics AI enablement across the entire engineering lifecycle. The company partners with leading organisations in aerospace & defence, automotive, semiconductors, materials, and energy & renewables, supporting them on some of their most critical and complex challenges.

PhysicsX is headquartered in the United Kingdom, with offices in London, New York, and Singapore and an expanding presence in the Bay Area.

You will build the LLM access layer for agents on the PhysicsX platform. That layer serves self-hosted open models and routes each task to the model, or combination of models, that best fits its constraints.

We want to bring deep, production experience in self-hosted LLM serving and model routing into Physics

X. You will establish the capability to run open models reliably and choose between self-hosted and external models according to the needs of each workload. You will set the technical direction for inference engines such as vLLM and SGLang, and serving platforms such as KServe. You will build foundations that product, delivery and customer teams can rely on.

You will own both. You will learn our requirements and define the implementation, rather than inherit a prescribed gateway design. You will wire serving and routing into the existing platform, identity and observability systems.

In the near term, you’ll make the platform’s agentic capabilities work out of the box, with no customer-supplied API key needed. Longer term, you’ll build the serving layer into a flexible routing foundation that balances competing priorities, such as cost, quality and data sensitivity, as our needs change.

• Make self-hosted serving and model routing work in air-gapped deployments, including sites where the hardware, connectivity and operational setup force constrained solutions.
• Own LLM packaging, registration, deployment, health checks, upgrades, rollback and retirement for supported LLMs.
• Evaluate and integrate serving platforms such as KServe, including inference services, serving runtimes, model loading and rollout controls.
• Define, model, and implement service levels for availability, quality, and performance metrics (e.g. time to first token, inter-token latency, and tail latency).
• Measure and improve throughput, GPU utilisation, memory use and cost per token under representative agent workloads.
• Tune continuous and chunked batching, KV and prefix caching, quantisation, speculative decoding and tensor, data or expert parallelism where appropriate.
• Evaluate advanced techniques such as cache-aware routing and prefill/decode disaggregation when workload evidence justifies their complexity.
• Build autoscaling, admission control and capacity management around GPU and serving signals rather than generic CPU metrics.
• Make every routing decision observable and explainable, and use evaluation evidence before adding specialist routes.
• Work with Research and Post-Training specialists to move candidate and tuned models through evaluation into supported production routes.
• Help determine how this capability should grow and, over time, shape the team around it.

• You have operated inference engines such as vLLM or SGLang, or serving platforms such as KServe, under production traffic, latency and reliability constraints.
• You can explain the boundary between serving orchestration and inference execution, including how deployment, scaling and runtime…