Senior Members of Technical Staff

We're hiring Senior Members of Technical Staff (MTS) who are strong software engineers and want to work close to production, customers, and the underlying systems powering enterprise AI.

You will be mapped to one of three technical tracks based on your strengths:

• AI Platforms: Enterprise AI platforms, backend systems, LLM applications and agents
• AI Infra: GPU infrastructure, Kubernetes, distributed systems and platform engineering
• Applied Research: AI evaluation, post-training, simulations and data curation

You don't need to choose a track upfront. Your technical depth and demonstrated experience will determine the best fit.

You have approximately 5 years of professional software engineering experience and a strong track record of shipping and operating production systems. You should be able to take ownership of a system or significant workstream end-to-end, from architecture and design through implementation, deployment, operations, debugging, and continuous improvement. We value strong engineering fundamentals, practical problem-solving, and the ability to make sound technical decisions in ambiguous environments.

Requirements:

• Strong programming ability in Python, Go, or Rust.
• Experience designing, building, and operating production services at scale.
• Strong understanding of APIs, databases, distributed systems, and software architecture.
• Hands-on experience with Docker and Kubernetes.
• Experience with at least one major cloud platform: AWS, Azure, or GCP.
• Strong understanding of Git, CI/CD, automated testing, and engineering best practices.
• Understanding of reliability concepts such as fault tolerance, retries, idempotency, failure handling, logging, monitoring, and observability.
• Ability to debug production issues, analyse system behaviour, and drive incidents through resolution.
• Ability to evaluate technical trade-offs and make architecture decisions with long-term maintainability in mind.

Track 1 AI Platforms

Best suited for backend and platform engineers who have hands-on experience building production applications and services around LLMs and enterprise AI

You should have experience with several of:

• Strong Python backend engineering
• REST/gRPC APIs and production services
• PostgreSQL / Redis and schema design
• Kafka, NATS, or other messaging systems
• Event-driven and distributed architectures
• LLM applications and production model APIs
• RAG pipelines
• Agentic applications and tool calling
• Vector databases
• LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, or equivalent frameworks
• LLM evaluation, observability, tracing, cost, and latency monitoring
• Enterprise AI integrations and platform engineering

You will work on

• RAG and multi-agent platforms
• Inference services
• Evaluation and benchmarking systems
• Enterprise integrations
• Metering and billing
• Governance and access control
• Audit, privacy, PII handling, and guardrails
• AI observability, tracing, and production monitoring
• Customer VPC and on-premises deployments

Track 2 AI Infra

Best suited for systems, infrastructure, and platform engineers interested in building large-scale GPU infrastructure for AI workloads

You should have experience with several of:

• Strong Go or Rust
• Linux systems and infrastructure engineering
• Kubernetes and container platforms
• Docker / containerd
• GPU infrastructure and fleet management
• CUDA
• NVML / DCGM
• MPS / MIG
• Kubernetes controllers and operators
• REST and gRPC platform APIs
• Kafka / NATS / RabbitMQ
• PostgreSQL / MySQL
• Prometheus / Grafana / OpenTelemetry
• Distributed systems and fault tolerance
• Networking, resource isolation, and reliability engineering

You will work on

• GPU fleet management and control planes
• Schedulers and multi-tenant infrastructure
• GPU sharing and isolation
• GPU health monitoring and recovery
• Kubernetes controllers, exporters, daemons, and platform services
• On-premises and cloud GPU infrastructure
• Infrastructure reliability and observability at scale
• Experience with GPU infrastructure, bare-metal systems, Kubernetes platform engineering, or HPC environments is particularly valuable.

Track 3 Applied Research

Best suited for ML engineers who combine strong software engineering fundamentals with hands-on experimentation and a strong bias toward research that reaches production.

You should have experience with several of:

• Strong Python and ML engineering fundamentals
• PyTorch or JAX
• LLM evaluation and benchmarking
• Fine-tuning / post-training
• Agent evaluation
• RL / RLHF
• Embedding and reranker experiments
• Prompt optimisation and tool-use training
• Simulation environments
• Synthetic data generation
• Large-scale data curation
• Model training or serving infrastructure
• Statistical experimentation and rigorous evaluation

You will work on

• Offline and online evaluation systems
• LLM-as-a-judge and regression frameworks
• Agent reasoning, planning, and tool-use improvements
• Fine-tuning and post-training
• Retrieval quality improvements
• Simulation environments for agent testing
• Data curation and synthetic data pipelines
• Production-ready AI research capabilities

What Success Looks Like

As a Senior MTS, you will:

• Own systems and significant technical workstreams end-to-end
• Drive projects from ambiguous requirements to production
• Make architecture decisions and clearly articulate technical trade-offs
• Ship reliable production software used by enterprise customers
• Operate and improve systems at scale
• Debug complex production issues and drive root-cause resolution
• Work closely with senior engineers, founders, and customers
• Mentor engineers and raise the technical bar within the team
• Contribute across engineering, infrastructure, AI, and customer-facing requirements
• Continuously expand your technical and business scope

Who Will Stand Out

The strongest candidates will demonstrate:

• Real production ownership across design, deployment, operations, and improvement
• Strong coding, architecture, and distributed systems fundamentals
• Experience operating production systems at scale
• Hands-on Docker, Kubernetes, and cloud experience
• Strong expertise in at least one of the three Aion tracks
• Ability to explain technical decisions, constraints, and trade-offs
• Strong debugging and problem-solving skills
• Ability to work independently in ambiguous environments
• Experience mentoring engineers and influencing technical direction
• Strong communication and collaboration skills
• A founder-like bias for action and ownership
• Ability to connect technical decisions to business and customer outcomes
• Comfort working in a fast-moving, early-stage startup environment