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Senior Data Software Engineer

Quantum computing holds the promise of humanity’s mastery over the natural world, but only if we can build a real quantum computer. PsiQuantum is on a mission to build the first real, useful quantum computers, capable of delivering the world-changing applications that the technology has long promised. We know that means we will need to build a system with roughly 1 million qubits that supports fault tolerant error correction within a scalable architecture, and a data center footprint.

By harnessing the laws of quantum physics, quantum computers can provide exponential performance increases over today’s most powerful supercomputers, offering the potential for extraordinary advances across a broad range of industries including climate, energy, healthcare, pharmaceuticals, finance, agriculture, transportation, materials design, and many more.

PsiQuantum has determined the fastest path to delivering a useful quantum computer, years earlier than the rest of the industry. Our architecture is based on silicon photonics which gives us the ability to produce our components at Tier-1 semiconductor fabs such as GlobalFoundries where we leverage high-volume semiconductor manufacturing processes, the same processes that are already producing billions of chips for telecom and consumer electronics applications. We also benefit from the quantum mechanics reality that photons don’t feel heat or electromagnetic interference, allowing us to take advantage of existing cryogenic cooling systems and industry standard fiber connectivity.

In 2024, PsiQuantum announced two government-funded projects to support the build-out of our first Quantum Data Centers and utility-scale quantum computers in Brisbane, Australia and Chicago, Illinois. Both projects are backed by nations that understand quantum computing’s potential impact and the need to scale this technology to unlock that potential. And we won’t just be building the hardware, but also the fault tolerant quantum applications that will provide industry-transforming results.

Quantum computing is not just an evolution of the decades-old advancement in compute power. It provides the key to mastering our future, not merely discovering it. The potential is enormous, and we have the plan to make it real. Come join us.

There’s much more work to be done and we are looking for exceptional talent to join us on this extraordinary journey!

Team Overview

PsiQuantum’s Quantum Applications Software Team is dedicated to creating a software environment where quantum application developers can discover, build, utilize, optimize, and visualize large-scale quantum algorithms for classically intractable problems. As we continue to scale, we’re building out a QA function that bridges classical software QA best practices with cutting-edge quantum software development.

Role Overview

We seek a Senior Data Software Engineer with deep expertise in data engineering to architect, build, and maintain scalable, flexible data pipelines and systems that support critical quantum software applications. These pipelines will enable classical pre-processing on high-performance computing (HPC) platforms and other data-intensive operations. In this hands-on role, you will bridge engineering and scientific research—gathering and analyzing requirements, collaborating with scientists to understand existing data operations, and using that knowledge to build foundational data infrastructure and frameworks. You will champion engineering excellence and design-first thinking across the department, while partnering with Product and other software engineering teams to establish robust data architecture and systems.

You’ll have the opportunity to:

  • Collaborate with key stakeholders to gather requirements, incorporate feedback, and produce engineering design documents that unify user and system needs.
  • Build and own critical data infrastructure using modern data technologies (e.g., Databricks, AWS Glue/Athena, AWS Redshift, Snowflake) to enable secure, large-scale operations.
  • Champion data engineering best practices by automating data workflows and implementing proactive monitoring for reliability and performance.
  • Partner with cross-functional teams (QA, Product, Engineering) to ensure compliance, scalability, and high availability of the data platform.

Responsibilities:

  • Develop and refine data processing pipelines to handle complex scientific or computational datasets.
  • Design and implement scalable database solutions to efficiently store, query, and manage large volumes of domain-specific data.
  • Refactor and optimize existing codebases to enhance performance, reliability, and maintainability across various data workflows.
  • Collaborate with cross-functional teams (e.g., research scientists, HPC engineers) to support end-to-end data solutions in a high-performance environment.
  • Integrate workflow automation tools, ensuring the smooth operation of data-intensive tasks at scale.
  • Contribute to best practices for versioning, reproducibility, and metadata management of data assets.
  • Implement Observability: Deploy monitoring/logging tools (e.g., CloudWatch, Prometheus, Grafana) to preempt issues, optimize performance, and ensure SLA compliance.

Experience/Qualifications:

  • Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
  • 8+ years in Data Engineering with hands-on cloud and SaaS experience.
  • Proven experience designing data pipelines and workflows, preferably for high-performance or large-scale scientific computations.
  • Strong knowledge of database design principles (relational and/or NoSQL) for complex or high-volume datasets.
  • Proficiency in one or more programming languages commonly used for data engineering (e.g., Python, C++, Rust).
  • Hands-on experience with orchestration tools such as Prefect, Apache Airflow, or equivalent frameworks.
  • Hands-on experience with cloud data services, e.g. Databricks, AWS Glue/Athena, AWS Redshift, Snowflake, or similar.
  • Excellent teamwork and communication skills, especially in collaborative, R&D-focused settings.

Preferred Qualifications:

  • Knowledge and experience with containerization and orchestration tools such as Docker and Kubernetes and event-driven architectures.
  • Knowledge of HPC job schedulers (e.g., Slurm, LSF, or PBS) and distributed computing best practices is a plus.
  • Experience with Infrastructure as Code (IaC) tools like Terraform, AWS CDK, etc.
  • Deployed domain-specific containerization (Apptainer/Singularity) or managed GPU/ML clusters.

PsiQuantum provides equal employment opportunity for all applicants and employees. PsiQuantum does not unlawfully discriminate on the basis of race, color, religion, sex (including pregnancy, childbirth, or related medical conditions), gender identity, gender expression, national origin, ancestry, citizenship, age, physical or mental disability, military or veteran status, marital status, domestic partner status, sexual orientation, genetic information, or any other basis protected by applicable laws.

Note: PsiQuantum will only reach out to you using an official PsiQuantum email address and will never ask you for bank account information as part of the interview process. Please report any suspicious activity to recruiting@psiquantum.com.

We are not accepting unsolicited resumes from employment agencies.

Average salary estimate

$135000 / YEARLY (est.)
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$120000K
$150000K

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What You Should Know About Senior Data Software Engineer, Psiquantum

At PsiQuantum, we're on an ambitious quest to unlock the true potential of quantum computing, and we're looking for a Senior Data Software Engineer to join our innovative team in sunny Palo Alto, California. If you're passionate about data engineering and have a flair for creating scalable data solutions, you might be the perfect fit! In this hands-on role, you'll have the opportunity to architect and maintain data pipelines that are critical for the development of quantum applications. You’ll collaborate closely with scientists and cross-functional teams, using your expertise to gather requirements and design robust data infrastructures. Your work will enable us to leverage the power of high-performance computing to tackle complex scientific challenges. Here, your contributions won't just be seen; they will be essential in shaping the future of technology. With an emphasis on engineering excellence and a design-first mindset, you will champion best practices that'll ensure smooth, reliable operations. You will also utilize modern data technologies like AWS and Databricks to build and secure large-scale operations. If you thrive in a collaborative environment, love tackling big challenges, and want to be part of a team that is truly making waves in the industry, come join us at PsiQuantum. There's plenty of exciting work ahead, and we can’t wait to see what you'll bring to the table!

Frequently Asked Questions (FAQs) for Senior Data Software Engineer Role at Psiquantum
What are the key responsibilities of a Senior Data Software Engineer at PsiQuantum?

As a Senior Data Software Engineer at PsiQuantum, your role involves architecting and maintaining large-scale data pipelines that support critical quantum software applications. You will work alongside scientists to gather and analyze requirements, ensuring that your data infrastructure meets operational needs while facilitating classical pre-processing on high-performance computing platforms.

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What qualifications are needed to apply for the Senior Data Software Engineer position at PsiQuantum?

To be considered for the Senior Data Software Engineer role at PsiQuantum, you should possess a Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, along with 8+ years of experience in Data Engineering. A strong familiarity with cloud and SaaS environments, coupled with proficiency in programming languages such as Python or C++, is essential.

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What types of technologies will I be working with as a Senior Data Software Engineer at PsiQuantum?

At PsiQuantum, you will engage with cutting-edge data technologies, including AWS Glue, Databricks, Redshift, and Snowflake. You’ll also utilize orchestration tools like Apache Airflow and modern database design principles in both relational and NoSQL contexts, all while implementing data engineering best practices.

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How does a Senior Data Software Engineer at PsiQuantum contribute to quantum application development?

Senior Data Software Engineers at PsiQuantum significantly contribute to quantum application development by building the foundational data infrastructure needed for large-scale quantum algorithms. Your expertise will help streamline data processing workflows and enhance the reliability and performance of data-intensive applications, ultimately allowing scientists to focus on groundbreaking quantum research.

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What opportunities for advancement does PsiQuantum offer for a Senior Data Software Engineer?

At PsiQuantum, there are ample opportunities for advancement for a Senior Data Software Engineer. As you develop your skills in cutting-edge quantum technologies and data engineering practices, you’ll be encouraged to take on leadership roles, mentor junior engineers, and contribute to pioneering projects that could transform entire industries.

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Common Interview Questions for Senior Data Software Engineer
Can you explain your experience with data pipelines and how it relates to the Senior Data Software Engineer role?

When discussing your experience with data pipelines, highlight specific projects where you've designed and implemented scalable data workflows. Focus on the technologies you used and the outcomes achieved, emphasizing how your work aligns with the goals of the Senior Data Software Engineer role at PsiQuantum.

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What strategies do you employ to optimize database performance?

To optimize database performance, discuss techniques such as indexing, query optimization, and caching strategies. Provide examples of how you've successfully implemented these strategies in past projects, and explain their impact on performance and reliability.

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How do you approach cross-functional collaboration in your projects?

Explain your communication style and provide examples of how you've effectively worked with cross-functional teams, such as research scientists and QA engineers. Emphasize the importance of gathering requirements and feedback throughout the project lifecycle.

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Describe your experience with cloud data services and how you've utilized them in previous roles.

Discuss your hands-on experience with cloud data services like AWS Glue, Databricks, or similar technologies. Provide specific examples of projects where you leveraged these services for data ingestion, transformation, or storage, and the benefits they brought to the project.

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What are best practices for versioning in data engineering, and how have you applied them?

Talk about your knowledge and application of best practices for versioning data assets, such as using Git for code changes and ensuring that data transformations are clearly documented. Share experiences of how these practices have enhanced project reproducibility and collaboration.

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Can you explain how you ensure the reliability of data workflows?

Discuss the automated monitoring tools and alert systems you've implemented to ensure reliable data workflows. Provide examples of how proactive monitoring has helped you identify and address issues before they became critical problems.

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What challenges have you faced with large datasets, and how did you overcome them?

Describe specific challenges encountered with large datasets, such as performance issues, data quality, or scalability. Highlight the solutions you implemented to overcome these challenges, showcasing your problem-solving skills.

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How do you stay updated with the latest trends in data engineering?

Discuss your strategies for keeping current with industry trends, such as following relevant publications, attending conferences, and participating in online communities. This demonstrates your commitment to continuous learning and staying at the forefront of data engineering.

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Describe your experience with orchestration tools like Apache Airflow.

Describe specific instances where you've utilized orchestration tools, such as Apache Airflow, to manage data workflows. Explain how you set up tasks, managed dependencies, and ensured the successful execution of complex workflows.

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What motivates you to work in the quantum computing space?

Share your genuine motivation for wanting to work in quantum computing, such as a passion for innovation, the desire to tackle complex scientific challenges, or an interest in contributing to technology that could change industries. Authenticity in your response can resonate well with interviewers.

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Full-time, on-site
DATE POSTED
March 29, 2025

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