Role

We’re looking for an exceptional Senior Full-Stack Software Engineer to work hand in hand with our Product team to build polished, high-performance web applications using React with FastAPI on the backend and Postgres as a database.

The ideal candidate leads with deep frontend expertise in building sophisticated, stateful UIs in React/TypeScript, while bringing enough backend fluency to own features end-to-end. You'll build rich, data-dense interfaces for scientific workflows where getting the interaction design and state management right is the hard part. Additionally, the ideal candidate is an 'AI native' and uses their strong technical expertise to augment the prototyping speed provided by LLMs. You'll ship deliverables in days, not months, and have a sharp instinct for system architectures that achieve rapid delivery and robust performance.

You will be a core contributor to shaping our next-generation biotech platform that discovers and tests new drugs on lab-grown human tissues.

Own the building of scientific experiment design and results-monitoring tools that, together with our platform, have enabled Vivodyne to partner with a majority of the top-10 pharmaceutical companies in making better, safer cures to complex human diseases. You will be expected to deliver web applications that align with well-designed frames/user flows and product requirements that help scientists interact seamlessly with our unique datasets, robotic systems, and lab-grown tissues.

You’ll have the opportunity to work at the cutting edge of biotech and software engineering, collaborating with teams spanning tissue engineering, robotics, and AI. You'll be working a few feet away from large precision robots, laser microscopes, and robot arms, surrounded by an exceptionally talent-dense team of experts from multiple scientific disciplines.

You will be able to focus on building internal products and tools with incredible functionality, surrounded by sophisticated end users directly appreciative of the products you build---without needing to do integration testing for first-gen Chromebooks or handle bug reports from Fire tablets.

While bringing a passion and exquisite skill for building and shipping high-quality applications that drive scientific discovery, you'll be involved firsthand in the advancement of human health, you'll watch thousands of human organ tissues grow before your eyes, and join an intense, fun, fast-moving team in shaping the future of human health.

Responsibilities

Frontend-Led Full-Stack Development — Architect and build complex React/TypeScript interfaces for scientific workflow applications; own backend integration points via API design and data layer work in Postgres.

User Experience — Create clean, responsive, and user-focused interfaces that translate complex requirements into intuitive designs, establishing best practices for component architecture and state management patterns.

Code Quality & Best Practices — Write well-tested, maintainable code with a focus on scalability, performance, and cost-efficiency.

Collaboration & Communication — Work closely with scientists, engineers, and AI researchers to gather requirements, provide updates, and deliver impactful solutions.

High Velocity / AI Accelerated Development — Leverage leading language models and agentic tools like Claude Code and Codex, or similar to ship reliable code quickly; maximize prototyping and component design velocity while balancing robustness of implementations

Requirements and Expectations

Technical Excellence

• Deep expertise in React, TypeScript, and modern frontend architecture (component composition, state management, performance profiling). 
• Proficient in building visually appealing, high-performance React applications.
• Strong CSS/styling architectural skills (CSS modules, Tailwind etc.)
• Strong data modeling skills for biotech applications, translating scientific needs into structured schemas.
• Comfortable in API design, schema definition, and query optimization with Postgres.
• Solid foundation in data structures, algorithms, and software architecture (CS degree preferred).

Accountability & Problem-Solving

• Own full-stack development, ensuring high-quality, tested, and timely feature delivery.
• Balance speed and rigor to solve complex engineering challenges, from large-scale data ingestion to optimizing imagery metadata.

Collaboration & Communication

• Work cross-functionally with scientists, engineers, and AI researchers, aligning technical solutions with user needs.
• Keep end-users in mind, delivering intuitive, efficient workflows.

Delivering Results

• Manage time effectively, handling multiple priorities while shipping production-ready features.
• Champion best practices, maintain clear documentation, and uphold high coding standards.

Architecture & Design

• Design scalable, reliable systems while anticipating and addressing bottlenecks.
• Leverage open-source and existing frameworks to optimize development.

Leadership & Influence

• Mentor junior engineers through code reviews and knowledge sharing.
• Advocate for full-stack best practices to drive scientific and business impact.

Qualifications

• Bachelor’s or Master’s in Computer Science (or related field), or equivalent experience.
• 5+ years building complex React applications with sophisticated state management, not just simple CRUD interfaces.
• Fluency in TypeScript. Knowledge of TanStack ecosystem is a bonus.
• Experience Developing Applications that Render and Annotate Imagery is highly valued.
• Comfortable working with APIs and understanding the data layer.
• Working knowledge of Python (e.g. Pydantic, FastAPI).
• Working knowledge of Postgres in production environments.
• Experience in High-throughput Laboratory Automation or Robotics is highly valued.
• Excellent Communication Skills for conveying technical concepts and aligning stakeholder expectations.
• Project Delivery Experience in fast-paced environments; comfortable balancing shifting priorities and multiple projects.
• Proven Ability to collaborate across disciplines, from biology to automation to AI, delivering high-impact results.
• Track Record of shipping secure, maintainable, and well-tested code.

Vivodyne, Inc. is an equal opportunity employer. Vivodyne complies with all laws respecting equal employment opportunity and does not discriminate against applicants with regard to any protected characteristic as defined by federal, state, and local law.

PsiQuantum’s mission is to build the first useful quantum computers—machines capable of delivering the breakthroughs the field has long promised. Since our founding in 2016, our singular focus has been to build and deploy million-qubit, fault-tolerant quantum systems. 

Quantum computers harness the laws of quantum mechanics to solve problems that even the most advanced supercomputers or AI systems will never reach. Their impact will span energy, pharmaceuticals, finance, agriculture, transportation, materials, and other foundational industries. 

Our architecture and approach is based on silicon photonics. By leveraging the advanced semiconductor manufacturing industry—including partners like GlobalFoundries—we use the same high-volume processes that already produce billions of chips for telecom and consumer electronics. Photonics offers natural advantages for scale: photons don’t feel heat, are immune to electromagnetic interference, and integrate with existing cryogenic cooling and standard fiber-optic infrastructure. 

In 2024, PsiQuantum announced government-funded projects to support the build-out of our first utility-scale quantum computers in Brisbane, Australia, and Chicago, Illinois. These initiatives reflect a growing recognition that quantum computing will be strategically and economically defining—and that now is the time to scale. 

PsiQuantum also develops the algorithms and software needed to make these systems commercially valuable. Our application, software, and industry teams work directly with leading Fortune 500 companies—including Lockheed Martin, Mercedes-Benz, Boehringer Ingelheim, and Mitsubishi Chemical—to prepare quantum solutions for real-world impact. 

Quantum computing is not an extension of classical computing. It represents a fundamental shift—and a path to mastering challenges that cannot be solved any other way. The potential is enormous, and we have a clear path to make it real. 

Come join us. 

Job Summary:

*We are currently only reviewing applications of candidate's that have their Ph.D. or are currently enrolled in a Ph.D. program. 

Quantum architecture internships offer a chance to work in cutting edge research to move quantum computing from a theoretical concept to a real-world technology. As an intern you will work with our quantum architecture team to contribute to the development of a full-stack quantum computing architecture. Topics can include theory and modelling of linear optical networks, fusion-based quantum computing, fault tolerance, decoding methods, logical architecture, simulation and modeling methods.

Internships take place through ought the year with durations from 3 to 6 months. We offer placement opportunities in Palo Alto, California (United States), Brisbane (Australia) as well remote positions. Location options will be dependent on the internship project topic.

The internship role requires a challenging mix of creativity, analytical skills, and knowledge in one of the above areas. Enthusiasm for quantum computing is essential!

Responsibilities:

• Develop and study methods for fault-tolerant FBQC.
• Project topics could include: linear optical networks, FBQC protocols, quantum error correcting codes, decoding methods, simulation and modelling methods
• Model the origin and propagation of errors.
• Simulate and benchmark different protocols.
• Document and communicate results.

Experience/Qualifications:

• Degree in Physics, Math or Computer Science or equivalent required.
• Currently studying for, or completed a postgraduate degree related to linear optics, fault tolerance
  or another aspect of quantum computing.
• Knowledge of quantum information is required. Familiarity with some of the following is desirable: quantum networks, graph states, stabilizer methods, quantum error-correcting codes, linear optical quantum computing, error modelling, graphical calculus (e.g, tensor networks, ZX calculus).
• Experience programming in Python, C++ or similar languages.
• Competent use of collaborative software development tools (e.g., GitHub) is desirable.
• A creative and adaptable approach to problem solving.
• Ability to work effectively in a highly collaborative team.

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.

About this opportunity:

At Freenome, we are seeking a Senior Machine Learning Research Engineer to join the Machine Learning Science (MLS) team, within the Computational Science department. The ideal candidate has a strong knowledge in designing and building deep learning (DL) pipelines, and expertise in creating reliable, scalable artificial intelligence/machine learning (AI/ML) systems in a cloud environment. 

The MLS team at Freenome develops DL models using massive-scale genomic data that presents significant challenges for current training paradigms. The Senior Machine Learning Research Engineer will primarily be responsible for developing and deploying the infrastructure needed to support development of such DL models: enabling distributed DL pipelines, optimizing hardware utilization for efficient training, and performing model optimizations. As part of an interdisciplinary R&D team, they will work in close collaboration with machine learning scientists, computational biologists and software engineers to accelerate the development of state-of-the-art ML/AI models and help Freenome achieve its mission of reducing cancer mortality via accessible early detection.

The role reports to the Director of Machine Learning Science. This can be a hybrid role based in our Brisbane, California headquarters (2-3 days per week in office), or remote.

What you’ll do:

• Implement and refine DL pipelines on distributed computing platforms enhancing the speed and efficiency of DL operations including model training, data handling, model management, and inference.
• Collaborate closely with ML scientists and software engineers to understand current challenges and requirements and ensure that the DL model development pipelines you create are perfectly aligned with scientific goals and operational needs.
• Continuously monitor, evaluate, and optimize DL model training pipelines for performance and scalability.
• Stay up to date with the latest advancements in AI, ML, and related technologies, and quickly learn and adapt new tools and frameworks, if necessary.
• Develop and maintain robust and reproducible DL pipelines that guarantee that DL pipelines can be reliably executed, maintaining consistency and accuracy of results.
• Drive performance improvements across our stack through profiling, optimization, and benchmarking. Implement efficient caching solutions and debug distributed systems to accelerate both training and evaluation pipelines.
• Act as a bridge facilitating communication between the engineering and scientific teams, documenting and sharing best practices to foster a culture of learning and continuous improvement.

Must haves:

• MS or equivalent experience in a relevant, quantitative field such as Computer Science, Statistics, Mathematics, Software Engineering, with an emphasis on AI/ML theory and/or practical development.
• 5+ years of post-MS industry experience working on developing AI/ML software engineering pipelines.
• Proficiency in a general-purpose programming language: Python (preferred), Java, Julia, C, C++, etc.
• Strong knowledge of ML and DL fundamentals and hands-on experience with machine learning frameworks such as PyTorch, TensorFlow, Jax or Scikit-learn.
• In-depth knowledge of scalable and distributed computing platforms that support complex model training (such as Ray or DeepSpeed) and their integration with ML developer tools like TensorBoard, Wandb, or MLflow.
• Experience with cloud platforms (e.g., AWS, Google Cloud, Azure) and how to deploy and manage AI/ML models and pipelines in a cloud environment.
• Understanding of containerization technologies (e.g., Docker) and computing resource orchestration tools (e.g., Kubernetes) for deploying scalable ML/AI solutions.
• Proven track record of developing and optimizing workflows for training DL models, large language models (LLMs), or similar for problems with high data complexity and volume.
• Experience managing large datasets, including data storage (such as HDFS or Parquet on S3), retrieval, and efficient data processing techniques (via libraries and executors such as PyArrow and Spark).
• Proficiency in version control systems (e.g., Git) and continuous integration/continuous deployment (CI/CD) practices to maintain code quality and automate development workflows.
• Expertise in building and launching large-scale ML frameworks in a scientific environment that supports the needs of a research team.
• Excellent ability to work effectively with cross-functional teams and communicate across disciplines. 
  

Nice to haves:

• Experience working with large-scale genomics or biological datasets.
• Experience managing multimodal datasets, such as combinations of sequence, text, image, and other data.
• Experience GPU/Accelerator programming and kernel development (such as CUDA, Triton or XLA).
• Experience with infrastructure-as-code and configuration management.
• Experience cultivating MLOps and ML infrastructure best practices, especially around reliability, provisioning and monitoring.
• Strong track record of contributions to relevant DL projects, e.g. on github.
  

Benefits and additional information:

The US target range of our base salary for new hires is $161,925 - $227,325 You will also be eligible to receive equity, cash bonuses, and a full range of medical, financial, and other benefits depending on the position offered.  Please note that individual total compensation for this position will be determined at the Company’s sole discretion and may vary based on several factors, including but not limited to, location, skill level, years and depth of relevant experience, and education. We invite you to check out our career page @ freenome.com/job-openings/ for additional company information.  

Freenome is proud to be an equal-opportunity employer, and we value diversity. Freenome does not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, veteran status, or any other status protected under federal, state, or local law.

Applicants have rights under Federal Employment Laws.  

• Family & Medical Leave Act (FMLA)
• Equal Employment Opportunity (EEO)
• Employee Polygraph Protection Act (EPPA)

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