Head of Data & AI Platforms, Beijing

About the Beijing AI Center

The Beijing AI Center is a new strategic investment by AstraZeneca to accelerate drug discovery through AI. The center brings together AI researchers, computational scientists, and platform engineers to apply foundation models, agentic AI, and large-scale scientific computing to real R&D problems. Situated in one of the world’s most dynamic AI talent markets, it operates at the intersection of AI and biologics discovery, computational chemistry, and data-driven drug development.

About the Role

Help build AstraZeneca's Beijing AI Center from the ground up, and own how AI gets done there. This is the person who turns a new on-premises GPU cluster, a fast-growing team, and China's foundation-model ecosystem into platform capabilities that make drug-discovery science faster. You set the methods, standards, and tooling that sit between raw infrastructure and the scientists using it. Success is the adoption, scale, and reuse of those capabilities across Discovery teams, not the delivery of any single AI project.

The center runs on three teams that depend on each other: Discovery verticals (biologics engineering, computational chemistry) own the science; Data & AI Platforms - this role - owns the capabilities; and R&D IT owns the infrastructure. A dedicated on-premises GPU cluster provides the compute backbone, operated by IT and shaped by your standards.

As Enterprise AI's single point of accountability for the center, you are who global and local stakeholders come to when a capability needs standing up, a bottleneck removed, or an ad hoc problem solved. You lead through your team and through the product owners you direct rather than building everything yourself; what you bring personally is enough technical depth to set the bar and judge the work. You own the requirements, methods, tooling, and evaluation rigor that make Discovery and IT more productive. IT handles GPU provisioning, cluster operations, and networking; Discovery owns model architecture, training objectives, and scientific interpretation.

On the shape of the ideal candidate: this is a broad mandate, and we are not looking for equal mastery of all five areas below. We expect deep strength in two or three of them and credible command of the rest, with the judgment to lead the others through strong specialists.

What You Will Do

Five focus areas, each roughly a fifth of the mandate. Priorities will shift as the center scales.

1. Platform Leadership - Single Point of Accountability

• Be the single point of accountability for the Beijing AI center: triage needs, remove bottlenecks, and problem-solve across teams so it succeeds.
• Serve as the escalation and decision point for platform, data, and compute needs across the center, owning the resolution, not just routing it
• Proactively clear blockers spanning IT, Discovery, global platform teams, and external partners
• Run the standing cross-functional coordination where center priorities and trade-offs are decided
• Translate ambiguous, fast-moving priorities into a coherent platform delivery plan
• Represent platform capabilities to global AI leadership and ecosystem partners

Mandate: you are accountable for center-level platform outcomes, not just your team's deliverables. Where a need falls between teams, you own resolving it - usually through influence rather than formal authority.

2. Compute Enablement & Research-Computing Strategy

• Own the demand side of the compute interface with IT, and the engineering strategy that lands research workloads on shared infrastructure efficiently.
• Own the compute demand-and-supply picture: gather workload requirements, forecast demand, and map scenarios IT can plan and procure against
• Set the research-computing engineering strategy: how workloads are packaged, optimised, and served including inference and serving strategy (quantisation, batching, throughput) for the models the platform relies on as well as the scheduling and orchestration requirements handed to IT
• Define and enforce MLOps standards - experiment tracking, model registry, CI/CD for ML, environment and dependency management - so compute is productive out of the box
• Set the direction for a sound data foundation for AI (AI-ready pipelines, harmonisation, retrieval and embedding infrastructure for scientific and literature data), working with IT and the data organisation who own the data platform

Boundary with IT: IT operates the cluster - provisioning, hardware, networking, scheduling execution, vendors. You own the requirements, forecasting, MLOps standards, and the engineering approach that makes it productive for research.

3. Agentic AI Platform - Direction & Delivery

• Set the direction for the center's agentic AI platform and deliver it through a product owner and their team.
• Set the vision and roadmap: multi-agent orchestration, local agent development frameworks, and scientific workflow automation
• Direct delivery through a product owner and team - setting outcomes, holding delivery to account, and unblocking, rather than building it personally
• Deliver against measured impact: adoption, developer productivity, scientific enablement, and business value
• Judge what to build, buy, or partner for: evaluate the China LLM landscape (Qwen, DeepSeek, GLM, Moonshot) and assess local partnerships (e.g., Alibaba, Tencent, Baidu) for inference, agent hosting, and platform capabilities
• Set the methodology for evaluating agent reliability, safety, and scientific accuracy, and define - with the product owner and IT - who owns runtime guardrails and tool-use permissioning once agents touch sensitive systems

Boundary: IT provides hosting infrastructure; a product owner runs the hands-on build. You own the direction - what gets built and why, which LLMs and partners are selected, and how impact is measured.

4. AI Engineering - Standards, Evaluation & Depth

• Set the center's AI engineering bar: the methods, standards, and evaluation frameworks that make the work reproducible and comparable. You hold enough command of the methods to set the standard and judge the work - the depth is in the judgment, evidenced by a track record of the calls made, not in running every job yourself.
• Set the standards for fine-tuning, post-training, and inference optimisation of open-weight models - method selection, training configurations, reproducibility, checkpoint and seed management, result validation - and judge the team's work against them
• Direct the center's evaluation platform: test harnesses, metric dashboards, leaderboards, and evaluation-as-a-service - including the technical assessment behind which models to adopt, and evaluation of agent behaviour and tool-use reliability
• Curate domain-relevant benchmark suites with Discovery (biologics, computational chemistry): metrics, held-out test construction, and data-leakage controls
• Establish objective criteria to compare models, tools, and approaches, enabling evidence-based technology decisions

Boundary with Discovery: Discovery scientists select model architectures, define objectives, curate domain data, and interpret results. You provide the methods toolkit and engineering standards - you build the car; they drive it.

5. Team Leadership

• Build and lead the platform team, and set the technical culture.
• Recruit, develop, and retain the platform team; grow senior capability and set the technical quality bar
• Run the Beijing hiring pipeline: source from top local universities and the AI talent market; build a culture that attracts and keeps strong people in a competitive market
• Onboard and develop hires; establish technical mentorship with global platform leads
• Build an engineering culture with clear standards, knowledge sharing, and continuous capability development

Requirements

Experience

Deep, demonstrated experience in AI/ML platform engineering, data engineering, or applied AI at scale - shown by the platforms you have owned and the scale you have operated at, not by years alone

A track record of leading and developing technical teams (direct reports across FTE, contractor, and matrix arrangements)

Built at least one platform capability from scratch - not just maintained - that a research or science team actually adopted

Experience serving scientific or research teams (biopharma, genomics, or a similar domain preferred)

Technical

AI training methods with a clear emphasis on fine-tuning, post-training, and alignment of open-weight models, and inference optimisation - deep enough to set standards and judge others' work

Evaluation and benchmarking infrastructure for ML models and agents (harnesses, leaderboards, automated pipelines)

MLOps fundamentals - experiment tracking, model registry, CI/CD for ML - plus compute demand forecasting and capacity/scenario planning

Familiarity with the data foundation AI depends on: AI-ready pipelines, harmonisation, and retrieval/embedding infrastructure (data-platform ownership sits with IT and the data organisation)

Modern AI application development: LLM tooling, agent frameworks, multi-agent orchestration

Direction of internal AI platforms: roadmap, delivery through a product team, and impact measurement

China-Specific

Able to work on-site in Beijing full-time (an interim period based in Shanghai is acceptable while relocation completes). AstraZeneca will confirm right-to-work and relocation support as part of the process

Able to conduct technical discussions in Mandarin unassisted - the team, local stakeholders, and China-based partners work in Mandarin, so day-to-day technical leadership depends on it. Recent Mandarin-language technical work, or bilingual fluency, both meet this

Familiarity with the Chinese AI ecosystem: local LLMs (Qwen, DeepSeek, GLM), China cloud providers (Alibaba, Tencent, Huawei), academic institutions, and AI companies

Leadership

Operates credibly in a matrix organisation: a global reporting line alongside a local delivery mandate

Comfortable with ambiguity and acting as a single point of accountability - triaging needs and unblocking across teams

Drives adoption of platform capabilities and standards among teams that have alternatives and don't report in - through credibility and persuasion, not mandate

Strong stakeholder alignment across Discovery scientists, IT, global platform teams, and external partners

Makes a well-argued case for platform priorities and investment to senior stakeholders

Nice-to-Have

Biopharma domain knowledge (drug discovery, protein engineering, computational chemistry)

Assessing and managing external technology partnerships (cloud, LLM, or academic)

Navigating multi-org delivery models where platform, infrastructure, and science are separate teams

Data governance, security, and cross-border/IP handling for research data - a plus, given the environment, though the data organisation and legal hold primary accountability

AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorisation and employment eligibility verification requirements.