Member of Technical Staff

As a Senior MTS, you will own systems, not just individual components. You will be responsible for architecture, technical trade-offs, operational health, and driving workstreams from ambiguous requirements to production with minimal scaffolding. You will work closely with production systems, customers, and the underlying infrastructure. Depending on business needs, you may contribute across engineering, infrastructure, AI, and customer-facing problems.
Technical Tracks
Track 1: AI Platforms
Best suited for backend and platform engineers with hands-on experience building production applications and services around LLMs and enterprise AI.
Responsibilities:
Build RAG and multi-agent platforms
Develop inference services
Build evaluation and benchmarking systems
Develop enterprise integrations
Implement metering and billing systems
Build governance and access-control capabilities
Implement audit, privacy, PII handling, and guardrails
Develop AI observability, tracing, and production monitoring
Support customer VPC and on-premises deployments
Relevant Experience:
Strong Python backend engineering
REST/gRPC APIs and production services
PostgreSQL, Redis, and schema design
Kafka, NATS, or similar 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
Track 2: AI Infra
Best suited for systems, infrastructure, and platform engineers interested in building large-scale GPU infrastructure for AI workloads.
Responsibilities:
Build GPU fleet management and control planes
Develop schedulers and multi-tenant infrastructure
Implement GPU sharing and isolation
Build GPU health monitoring and recovery systems
Develop Kubernetes controllers, exporters, daemons, and platform services
Support on-premises and cloud GPU infrastructure
Improve infrastructure reliability and observability at scale
Relevant Experience:
Strong Go or Rust
Linux systems and infrastructure engineering
Kubernetes and container platforms
Docker/containerd
GPU infrastructure and fleet management
CUDA, NVML, DCGM, MPS, or MIG
Kubernetes controllers and operators
REST and gRPC platform APIs
Kafka, NATS, or RabbitMQ
PostgreSQL or MySQL
Prometheus, Grafana, or OpenTelemetry
Distributed systems and fault tolerance
Networking, resource isolation, and reliability engineering
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 focus on research that reaches production.
Responsibilities:
Build offline and online evaluation systems
Develop LLM-as-a-judge and regression frameworks
Improve agent reasoning, planning, and tool use
Work on fine-tuning and post-training
Improve retrieval quality
Develop simulation environments for agent testing
Build data curation and synthetic data pipelines
Turn AI research into production-ready capabilities
Relevant Experience:
Strong Python and ML engineering fundamentals
PyTorch or JAX
LLM evaluation and benchmarking
Fine-tuning and 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
Requirements:
Approximately 5 years of professional software engineering experience.
Strong track record of building and operating production systems.
Ability to own systems or significant workstreams end-to-end, from architecture and design through deployment, operations, debugging, and continuous improvement.
Strong engineering fundamentals and practical problem-solving skills.
Ability to make sound technical decisions in ambiguous environments.
Core Engineering Requirements:
Strong programming skills 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 including fault tolerance, retries, idempotency, failure handling, logging, monitoring, and observability.
Ability to debug production issues, analyse system behaviour, and drive incidents to resolution.
Ability to evaluate technical trade-offs and make maintainable architecture decisions.

Skills
Python, LLMs, Golang, Rust, Generative AI, Computer Vision, Machine Learning, LangChain, PyTorch, CUDA

Experience
5-9 yrs