Biohub

Security Engineer

• Designing Secure Architecture: Architecting and implementing company wide security standards, network and system security measures such as firewalls, encryption, and Identity and Access Management (IAM) tools.
• Testing and Vulnerability Management: Conducting routine penetration testing and vulnerability assessments to identify and patch security gaps before attackers can exploit them.
• Automating Security Processes: Scripting and deploying automated security tools, integrating scanning into CI/CD pipelines, and streamlining threat detection.
• Leverage and contribute to open source tools and technologies.
• Build observability systems to monitor performance, detect issues, debug and mitigate incidents; and participate in incident response and root cause analysis.

Staff Software Engineer, Security Engineering, AI Compute

Engineer

• Design, prototype, implement, and optimise custom subsystems for light-sheet microscopes and other advanced imaging platforms. Integrate mechanical, thermal, fluidic, optical, and electronic components into cohesive, reliable experimental systems.
• Develop modular hardware control software in Python (and occasionally C/C++), interfacing with cameras, stages, lasers, sensors, and other instrumentation components.
• Design and implement automated or “smart” imaging protocols incorporating feedback loops (e.g., sensor-driven control, adaptive imaging strategies, and real-time decision-making).
• Collaborate with scientists, bioengineers, computer scientists, and biologists to implement new features and capabilities, including hardware interfaces, acquisition modes, graphical user interfaces, smart automation, high-performance data visualization, and scalable data storage pipelines.
• Contribute to data acquisition workflows capable of handling large-scale imaging datasets, including performance optimization, structured data saving, and integration with downstream analysis pipelines.
• Document system architectures, mechanical designs, schematics, and assembly procedures to ensure reproducibility, maintainability, and scalability.

Optical Engineer, Advanced Light Sheet Microscopy

Data Engineer

• Design and build data pipelines that process genomic and imaging data at petabyte scale
• Solve performance and bandwidth challenges with creative engineering
• Build agent-based systems for automated dataset curation, quality control, and workflow generation
• Create tooling for data cataloging and registration that makes datasets discoverable and accessible
• Collaborate with AI Research teams to translate model requirements into data specifications, and with our scientists to integrate public and internal data into large-scale ai-ready datasets
• Improve pipeline reliability and observability, working toward 99%+ success rates without manual intervention

• Build and support a hybrid HPC-AI environment with large-scale on-prem compute/storage and elastic cloud GPU clusters (Coreweave, AWS, GCP).
• Architect and optimize environments for large-scale AI training and tuning, and low-latency scientific workloads.
• Integrate MLOps and model deployment pipelines into HPC infrastructure, ensuring reproducibility and efficiency.
• Implement advanced resource scheduling and orchestration (Slurm, Kubernetes, SUNK) optimized for mixed HPC and AI workflows.
• Support researchers with job optimization, GPU utilization best practices, and performance tuning for AI and HPC applications.
• Evaluate, deploy, and maintain AI/ML software stacks (e.g., PyTorch, TensorFlow, Hugging Face, RAPIDS) and HPC toolchains.
• Ensure robust data ingest, analysis, and management capabilities for AI and HPC workloads, including integration with parallel file systems and object storage.
• Work with diverse science teams to translate research requirements into hardware/software solutions, from experimental design through publication.
• Promote best practices for AI model training, validation, and deployment in shared computing environments.
• Foster a culture of shared learning by running internal workshops on HPC-AI tooling (e.g., VS Code remote dev, containerization, MLOps workflows).

Staff HPC Engineer

Data Scientist

• Set technical vision and strategy for the design of data representations and tokenization strategies across biological data types—including imaging, sequencing, and multimodal data—that enable novel model architectures
• Develop, deploy and validate approaches for combining heterogeneous data modalities into unified training frameworks, designing for robustness to noise, bias, and batch effects
• Evaluate model performance, identifying which biological signals are captured or lost and iterating to improve
• Partner deeply with ML engineers and AI researchers to co-design datasets and optimize model training, evaluation, and generalization
• Lead cross-functional initiatives spanning data engineering, infrastructure, science, and product, aligning technical execution with long-term scientific goals
• Identify and drive new data acquisition and generation opportunities, from consortium partnerships to internal experimental pipelines
• Serve as a technical mentor and leader, raising the bar for data science and ML rigor across the organization

Senior Staff Data Scientist, Virtual Biology Initiative – AI Research

Machine Learning Engineer

• Work with high-dimensional scientific data formats and contribute to backend compatibility, format evaluation, and I/O performance benchmarking at petabyte scale.
• Define and shape the engineering patterns your team and collaborating researchers will build on for years; the abstractions you write today become the foundation others depend on at scale.
• Work at the intersection of AI systems and biological discovery, where the infrastructure problems you solve directly determine what science becomes possible.
• Deploy models to production and manage artifact tracking across models and datasets.
• Design and optimize GPU-native data loading pipelines for large-scale multi-dimensional tensor workloads, including profiling and resolving hardware utilization bottlenecks across multi-backend systems.
• Work on simplification and improvement of codebase abstractions to accelerate research momentum.
• Build and maintain primitives for pre-training infrastructure that ensure the reliability and continuity of large-scale training runs.
• Help cultivate best practices in MLOps, and think about the full ML lifecycle, including data, fine-tuning, deployment, reliability and monitoring.
• Possesses the ability to execute complex modifications to the research pipeline, such as fast data loading and distributed training.
• Handle DevOps responsibilities, focused on making all engineers and researchers more productive. This includes tasks like cluster monitoring, unit testing and integration testing of research codebase, and continuous integration.
• Collaborate with partner researchers and engineers to deploy our technology within external infrastructure.

Machine Learning Engineer, AI

Application Engineer

• Lead the design, development, testing, and validation of automated protocols for cell biology (including multi-day cell culture and passaging), cell imaging, and molecular biology.
• Independently program and optimize methods on Hamilton STAR/Vantage platforms using VENUS software, and other platforms, such as Opentrons and Agilent Bravos.
• Partner with research scientists and technology teams to scope automation protocols, define requirements, and deliver solutions that meet scientific and throughput goals.
• Develop and maintain robust quality control processes for automated workflows, including run validation, error handling, and performance monitoring.
• Create and maintain comprehensive documentation including method specifications, SOPs, validation reports, and training materials.
• Troubleshoot and resolve, in collaboration with hardware and software engineers and/or scientists, complex hardware, software, and method issues.
• Manage vendor relationships for maintenance and support.
• Mentor junior automation engineers and contribute to the team's technical growth and best practices.
• Identify and implement process improvements that increase throughput, reliability, and data quality across the automation platform.

Senior R&D Engineer, Lab Automation Applications Engineer

Research Analyst

• Perform molecular biology workflows including bacterial culture, plasmid cloning, mini/midi preps, and construct verification to generate reagents for cell engineering experiments.
• Maintain mammalian cell lines (Jurkat, 293T, K562) including passaging, selection, and banking — serving as a reliable backbone for ongoing assay development.
• Produce and package lentiviral vectors, troubleshoot viral production workflows, and execute transduction experiments.
• Execute single-cell genomics and sequencing workflows, including 10x Genomics library preparation and next-generation sequencing library QC.
• Perform flow cytometry experiments for immune cell phenotyping and functional assays.
• Design, optimize, and troubleshoot experimental protocols and SOPs, proactively identifying bottlenecks and opportunities to improve throughput, reliability, or efficiency.
• Maintain rigorous documentation of protocols, experimental conditions, results, and reagent inventory and take ownership of lab operations logistics including ordering and cataloging.

Research Associate, NGS Assay Development

Full-stack Engineer

• Develop and maintain the tooling and infrastructure that drives the entire data lifecycle at Biohub, from ingestion and processing to secure storage and access.
• Partner with researchers and engineers across genetics, imaging, and literature, ensuring data accessibility and performance.
• Design and implement flexible, scalable, and performant systems leveraging technologies like Argo Workflows, Slurm, Ray, and AWS Parallel Cluster.

Staff Software Engineer, Security Engineering, AI Compute

Optical Engineer, Advanced Light Sheet Microscopy

Data Engineer

Staff HPC Engineer

Senior Staff Data Scientist, Virtual Biology Initiative – AI Research

Machine Learning Engineer, AI

Machine Learning Engineer, AI

Senior R&D Engineer, Lab Automation Applications Engineer

Research Associate, NGS Assay Development

Staff Software Engineer – Data Infrastructure, AI Compute Platform