Senior Software Engineer, Inference Platform

United States Digital Space LLC; Greater London; Hybrid; GBP 120,000 - 165,000; Full time

Job summary

United States Digital Space LLC is seeking a seasoned engineer to build and scale an AI inference platform serving thousands of requests per second with sub-second latency.

You will own architecture for gateway, orchestration, runtimes, and autoscaling, while optimizing GPU usage and deployment pipelines, collaborating across product and customer-facing teams.

Qualifications

• 4+ years building and scaling backend or inference infra.
• Experience with AI/ML inference systems and engines.
• Distributed systems, microservices, API gateway patterns.
• Golang preferred; other languages OK for performance components.

Responsibilities

• Design and build the inference service platform for scalable AI models.
• Own core components: AI Gateway, Orchestrator, Engines, Autoscaler.
• Optimize deployment pipelines and zero-downtime upgrades.
• Ensure low-latency, high-throughput inference and observability.

Skills

Golang

Distributed systems

Low-latency design

Performance optimization

Cloud/nc-scale inference

API gateway patterns

Tools

Kubernetes

Docker

Prometheus

Grafana

TensorRT/LLM stacks

Job description

About the companythe company is the enterprise AI platform, a full-stack solution for building, fine-tuning, and deploying AI at scale. Whether an organization is modernizing internal operations, launching AI-powered products, or transforming customer experiences, the company takes them from concept to production on a single, unified platform.

We work differently than most AI companies: our teams deploy alongside our customers, turning production-ready AI into real business outcomes in weeks, not quarters.

We’re a fast-growing, VC-backed startup led by founders with a track record of successful exits. With teams across the US, UK, and India, we’re building the next generation of enterprise AI and we’re looking for exceptional people to help us scale.

Who You Are

You’re a seasoned engineer who has built and scaled high-performance inference systems for AI/ML workloads. You understand the complexities of serving models at scale latency optimization, resource orchestration, autoscaling dynamics, and production reliability. You’ve designed distributed systems that handle thousands of requests per second while maintaining sub‑second response times and cost efficiency.

Experience with Golang is strongly preferred, and exposure to inference engines (vLLM, TGI, TensorRT), containerization, and distributed systems is an added bonus. You take ownership of platform‑level decisions, think strategically about performance vs. cost trade‑offs, and want your work to power AI inference for thousands of developers globally.

You’re product‑minded, you understand how your technical decisions impact developers using the company's platform and think about the end‑to‑end user experience. You’re a team player comfortable wearing multiple hats one day you’re optimizing inference latency, the next you’re joining customer calls to understand their deployment challenges, and the day after you’re helping with UI/UX, customer success, documentation and product ops.

What You’ll Do

Inference Platform Architecture & Core Services

• Design and build the company's inference service platform the backbone for serving AI models at scale across diverse workloads
• Own and architect core platform components: AI Gateway, Resource Orchestrator, Runtime Engines, and Autoscaler
• Design highly modular, scalable, and extensible low‑level designs (LLDs) for inference infrastructure components
• Lead high‑level design discussions, establish architectural patterns, and drive technical decision‑making for the inference stack

Model Deployment & Lifecycle Management

• Understand and optimize the dynamics of model deployment, version upgrades, and rollback strategies
• Build robust deployment pipelines for seamless model updates with zero‑downtime deployments
• Design intelligent routing systems for multi‑model serving, A/B testing, and canary deployments
• Implement strategies for efficient GPU utilization and model cold‑start optimization

Performance & Distributed Systems

• Implement highly performant and optimized software for low‑latency, high‑throughput inference serving
• Build and debug production‑grade code in distributed systems handling real‑time AI workloads
• Optimize inference pipelines for latency, throughput, batching efficiency, and resource utilization
• Design fault‑tolerant systems with graceful degradation and automatic recovery mechanisms

Observability & Engineering Excellence

• Build high‑performance telemetry and observability stack for inference metrics, performance tracking, and debugging
• Implement comprehensive monitoring for model latency, throughput, error rates, GPU utilization, and cost per inference
• Conduct thorough code reviews to maintain code quality, performance standards, and architectural consistency

Establish engineering best practices for testing, documentation, and production readiness.

Requirements

Technical Skills & Experience* 4+ years of experience building and scaling backend systems, distributed platforms, or inference infrastructure

• Strong understanding of AI/ML inference systems and experience with inference engines (vLLM, TGI, TensorRT‑LLM, or similar)
• Deep knowledge of distributed systems design, microservices architecture, and API gateway patterns
• Proficiency in Golang strongly preferred; Python, Rust, C++ for performance‑critical components a plus
• Experience with container orchestration (Kubernetes, Docker) and infrastructure‑as‑code
• Solid understanding of autoscaling strategies, load balancing, and resource scheduling algorithms
• Experience building high‑throughput, low‑latency systems with sub‑100ms response time requirements
• Familiarity with message queues (Kafka, RabbitMQ), databases (PostgreSQL, Redis), and event‑driven architectures
• Knowledge of GPU computing, model serving optimizations (batching, quantization, multi‑tenancy), and resource allocation
• Experience with observability tools (Prometheus, Grafana, OpenTelemetry) and distributed tracing
• Understanding of API design, rate limiting, authentication/authorization, and security best practices
• Exposure to AI model deployment workflows and model lifecycle management is highly desirable

Bonus / Good to Have

HPC & Cluster Management: Experience handling large‑scale HPC clusters using Kubernetes and Slurm for job scheduling, resource allocation, and workload orchestration

• Data Engineering: Expertise with data pipelines, ETL systems, and large‑scale data processing frameworks
• Systems-Level Programming: Experience with low‑level systems programming such as storage systems, Kubernetes operators, OS-level software development, or daemon services (llm‑d, system agents)
• ML Platform Engineering: Experience productionizing ML pipelines, batch job orchestration, model fine‑tuning workflows, and Jupyter notebook orchestration systems
• Enterprise Deployment: Experience platformizing and packaging software for on‑premises deployments or customer VPC installations with emphasis on security, compliance, and operational simplicity

Benefits

Preferred Attributes

• High ownership, self‑driven and biased for action.
• Strong strategic thinking and ability to connect technical decisions to business impact.
• Excellent communication and mentoring skills.
• Thrives in ambiguity, fast‑paced environments, and early-stage startup culture.

Why Join the company?

• Work directly with high‑pedigree founders shaping technical and product strategy.
• Build infrastructure powering the future of AI computers globally.
• Significant ownership and impact with equity reflective of your contributions.
• Competitive compensation, flexible work options, and wellness benefits