Member of Technical Staff

Member of Technical Staff (MTS) is how engineers join the team. Rather than hiring against narrow job titles, strong engineers are mapped to the track where their skills and expertise can have the greatest impact. 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 systems with minimal scaffolding.

You will work close to production, customers, and the underlying infrastructure. You may contribute across engineering, infrastructure, AI, and customer-facing problems depending on business needs. Candidates are considered for one of three tracks: AI Platforms, AI Infra, or Applied Research, and are mapped to the most relevant track based on demonstrated skills and expertise during the interview process.

Responsibilities:

• 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 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.
• For AI Platforms: work on RAG and multi-agent platforms, inference services, evaluation and benchmarking systems, enterprise integrations, metering and billing, governance and access control, audit and privacy controls, AI observability, and customer VPC or on-premises deployments.
• For AI Infra: work on GPU fleet management and control planes, schedulers and multi-tenant infrastructure, GPU sharing and isolation, GPU health monitoring and recovery, Kubernetes controllers and platform services, and on-premises and cloud GPU infrastructure.
• For Applied Research: work on offline and online evaluation systems, LLM-as-a-judge and regression frameworks, agent reasoning and tool-use improvements, fine-tuning and post-training, retrieval quality improvements, simulation environments, and synthetic data pipelines.

Requirements:

• Approximately 4+ years of professional software engineering experience with a strong track record of shipping and operating production systems.
• Ability to take ownership of a system or significant workstream end-to-end, from architecture and design through implementation, deployment, operations, debugging, and continuous improvement.
• Strong engineering fundamentals, practical problem-solving, and the ability to make sound technical decisions in ambiguous environments.
• 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, analyze system behavior, and drive incidents through resolution.
• Ability to evaluate technical trade-offs and make architecture decisions with long-term maintainability in mind.
• Strong communication and collaboration skills.
• Ability to work independently in ambiguous environments.
• Experience mentoring engineers and influencing technical direction.
• Ability to connect technical decisions to business and customer outcomes.
• Comfort working in a fast-moving, early-stage startup environment.

Nice To Have:

• AI Platforms: Experience with Python backend engineering, REST or gRPC APIs, PostgreSQL or Redis, Kafka or NATS, event-driven and distributed architectures, LLM applications, RAG pipelines, agentic applications, vector databases, frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, or AutoGen, LLM evaluation and observability, and enterprise AI integrations.
• AI Infra: Experience with Go or Rust, Linux systems, Kubernetes and container platforms, Docker or containerd, GPU infrastructure and fleet management, CUDA, NVML or DCGM, MPS or MIG, Kubernetes controllers and operators, platform APIs, messaging systems, SQL databases, Prometheus, Grafana, OpenTelemetry, networking, resource isolation, and reliability engineering. Experience with bare-metal systems, Kubernetes platform engineering, or HPC environments is particularly valuable.
• Applied Research: Experience with Python and ML engineering, PyTorch or JAX, LLM evaluation and benchmarking, fine-tuning or post-training, agent evaluation, RL or RLHF, embedding and reranker experiments, prompt optimization, simulation environments, synthetic data generation, large-scale data curation, model training or serving infrastructure, and statistical experimentation.
• Demonstrated production ownership across design, deployment, operations, and improvement.
• Strong coding, architecture, and distributed systems fundamentals.
• Hands-on experience operating production systems at scale.
• Strong expertise in at least one of the three tracks.