Jobs at Allen Institute

Designs, builds, and maintains scalable cloud-based data infrastructure for large-scale scientific and biological datasets. Responsibilities include developing databases, APIs, metadata standards, data migration scripts, processing pipelines, monitoring infrastructure, and data-sharing systems. The role collaborates with scientists, product managers, and engineers; provides technical mentorship; and promotes software engineering best practices through testing, code reviews, documentation, and version control.

Leads the Molecular Tool Development automation group, setting strategy and managing staff, vendors, budgets, and resources. Translates complex molecular laboratory workflows into scalable, validated liquid-handling automation solutions. Oversees requirements, scripting, platform integration, validation, deployment, support, and technical standards. Partners with scientists and engineers across proteomics, cell assays, and NGS programs, while developing training, mentoring staff, and advancing high-throughput, reproducible research capabilities.

Own and evolve the Allen Institute’s multi-cloud AI compute platform, including Kubernetes control planes, operators, APIs, web UI, CLI, telemetry, security, and compliance. Build reliable infrastructure across AWS and GCP for scientific AI workloads, establish production-quality engineering practices, and collaborate with scientists, engineers, and vendors. Develop toolkits supporting agentic science, data access, and AI-augmented research.

Conduct machine learning research on large-scale neural, behavioral, EMG, and video datasets. Develop and evaluate latent-variable, regression, classification, dimensionality-reduction, and signal-processing models to understand neural population coding of movement. Produce reproducible Python analyses, communicate findings, and contribute to scientific presentations and publications while working under mentorship in a collaborative neuroscience research environment.

Lead and optimize the organization’s contracts program, managing the full contract lifecycle and administering the CLM platform. Draft and negotiate research, services, consulting, and commercial agreements; maintain templates, playbooks, workflows, documentation, and service standards; implement technology and AI process improvements; collaborate with Legal on risk strategies; and supervise contract specialists supporting internal and external stakeholders.

Build and maintain machine learning training, evaluation, data access, and model infrastructure for biological research. Responsibilities include designing large-scale data storage and versioning systems, maintaining a model zoo, supporting lab-in-the-loop training, making build-versus-buy decisions, and collaborating with AI scientists to develop novel models.

Build, deploy, and maintain cloud and on-premises infrastructure for high-throughput data pipelines. Define Linux configuration as code (Ansible), manage Terraform-based AWS infrastructure, support CI/CD, databases, logging and metrics, troubleshoot production/staging issues, and participate in on-hours support rotations.

Manage a large Linux-based scientific computing environment, including servers, workstations, storage, cloud infrastructure, patching, cybersecurity agents, monitoring, lifecycle management, and performance scaling. Design and deploy infrastructure services, automate operations with shell or Python, develop system standards, and support highly available services. The role includes rotating evening and weekend on-call coverage, occasional additional hours, and local travel to data centers or other institute locations.

Lead institute-wide data governance, metadata standards, and lifecycle management for large multimodal biological datasets. Own data stewardship, cataloging, and compliance (NIST/HIPAA), drive adoption of standards, advise scientific and technical leaders, and lead cross-functional initiatives to improve discoverability, reuse, and long-term data sustainability.

Lead scientific computing strategy for Neural Dynamics: scale petabyte data platforms, build performant data pipelines, apply state-of-the-art ML and foundation models to multimodal biological data, establish engineering best practices, form strategic partnerships, and manage engineering teams to enable open-science discovery in neuroscience.

Serve as strategic legal advisor to executive leadership and the Board, lead the in-house legal team, and oversee governance, compliance, risk management, and enterprise legal operations. Advise on nonprofit and tax-exempt law, AI governance, IP and technology transactions, export controls, sponsored research, data privacy, cybersecurity, employment, and federal contracting. Negotiate complex agreements, manage outside counsel, and build scalable legal processes to enable scientific innovation and institutional mission.

Architect, build, and operate cloud and on‑prem AI/ML training and inference platforms (including federated learning). Partner with scientists and external collaborators, benchmark and optimize ML infrastructure, ensure security/compliance and cost effectiveness, mentor engineers, and represent the Institute in vendor and cross‑institutional technical engagements.

Design, train, and deploy large-scale AI/ML models for multimodal biological data; build research-grade ML infrastructure and data pipelines; collaborate with scientists and engineers; establish best practices and community standards; publish and open-source methods and code.