Senior Engineer - ML & AI Systems

Description

You will build the production services, pipelines and infrastructure behind AI features used by millions of small businesses every day, including LLM-driven and agentic experiences. This is a software engineering role focused on building and running systems, not training or tuning models.

You will join the AI Products team, a diverse, globally distributed group of scientists, engineers, product managers and analysts within the Data & AI business. Engineers work with Applied Scientists early to build the interfaces and harnesses that move models into production, then own the engineering and infrastructure of each ML and AI system once it ships.

Current work includes running the platforms and services for traditional ML, generative AI and agentic systems; a conversational GenAI assistant that is becoming more context-aware and agentic; automated bank reconciliation and smart document capture that match transactions and extract data from bills and receipts; a platform where agents plan and carry out multi-step tasks across the company's products; and AI features integrated into customer-facing workflows along with their backend orchestration.

Responsibilities

• Design, build and own the production systems that run LLM-driven and agentic features, including services, pipelines and infrastructure.
• Make architecture decisions that determine scale, cost, reliability and how quickly teams can ship.
• Own the engineering and infrastructure of ML and AI systems in production, covering reliability, cost and scale.
• Work with Applied Scientists from the start to build the interfaces and harnesses that bring models into production.
• Set engineering standards, mentor other engineers and raise the capability of AI Products.
• Work with teams across the company to make data usable and systems dependable.

Requirements

• A record of designing, shipping and operating production backend systems at scale.
• Experience building and running production systems for LLM or agentic workloads, such as orchestration, tool use, evaluation, guardrails, latency and cost control.
• Strong system design skills and solid coding in Python, or another language for web-scale services such as Go, Java, Kotlin or TypeScript.
• Experience with API and service design, cloud infrastructure (ideally AWS), CI/CD and observability, with attention to latency, cost, reliability and SLOs.
• Experience running services in production, including on-call work and incident response.
• Solid SQL and experience processing large data sets.
• Clear communication, able to explain complex technical ideas to both business and technical audiences.
• A coaching approach, with the ability to set standards and mentor engineers.
• Experience putting ML or LLM workloads into operation, such as inference serving and evaluation pipelines.
• Familiarity with MLflow, PyTorch or TensorFlow, and data orchestration tools.
• Experience with distributed processing, such as Spark or Dask.
• Candidates who do not match every point are still encouraged to apply, as the company hires for skills, passion and the perspective each person brings.

Conditions

The role is hybrid, with set office days and shared 'boost days' in the office so teams can connect and ship code together, plus remote flexibility.

Access to modern office spaces.