C

Callosum

Orchestrates AI workloads across specialized silicon

Inference Performance & Deployment - Member of Technical Staff

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeLondon, UK

About the Job

Requirements

  • Experience deploying and benchmarking large model inference in production or production-equivalent environments.
  • Familiarity with multi-node GPU deployments and associated networking and communication stacks.
  • End-to-end performance characterization skills, including isolating bottlenecks in networks, runtimes, memory subsystems, or models.
  • Familiarity with serving frameworks such as Dynamo, Triton Inference Server, or similar orchestration layers.
  • Ability to translate performance data into actionable, prioritized feedback for teams building underlying systems.
  • A disciplined and systematic approach to deployment, including reproducibility, measurement methodology, and controlled comparisons.

Responsibilities

  • Run experiments self-hosting models on cloud instances or on-premises across providers and hardware configurations, systematically characterizing performance envelopes.
  • Develop and maintain deployment patterns that are reproducible, measurable, and optimized for latency, throughput, and cost.
  • Work on orchestration and routing software above the inference engine to improve caching, request scheduling, batching, and resource allocation.
  • Integrate accelerator support, engine features, and infrastructure upgrades, providing feedback on bottlenecks and essential capabilities and guiding stack optimizations.
  • Build and maintain benchmarking harnesses, regression suites, and performance dashboards that give the team a shared view of system health and progress.

About the Company

Callosum

Orchestrates AI workloads across specialized silicon

Callosum builds AI infrastructure that unifies different AI models and hardware chips to run workloads on the best combination of model and chip for each task. Its Tailored Inference APIs route tasks to the optimal model–chip pair while honoring cost, energy, and latency constraints, using partners like Cerebras and Rebellions for specialized silicon. Unlike approaches that rely on one model and uniform hardware, Callosum orchestrates multiple models and chips to maximize efficiency. The company's goal is to enable scalable, cost-efficient AI inference across heterogeneous hardware by intelligently matching tasks to the right combination of model and accelerator.

Company size
11-50
Company stage
Seed
Total funding
$110.3M
Headquarters
London, United Kingdom
Founded
2025

Recently Posted Jobs

Explore more opportunities at Callosum

See more jobs