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.

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:

Are you passionate about advancing quantum materials theory and computational modeling? As a Quantum Materials Theorist, you will join an interdisciplinary team at the forefront of computational physics, materials science, machine learning, and quantum technologies. This role offers the opportunity to develop and refine theoretical and computational approaches for understanding and predicting material properties, contributing to next-generation technologies.

You will bring expertise in electronic structure theory, many-body physics, and computational methods to explore complex quantum materials and emergent phenomena. Your work will involve integrating conventional and emerging computational approaches, incorporating insights from machine learning, high-performance computing (HPC), and quantum computing to drive innovation in materials discovery and design.

This position provides the opportunity to engage in high-impact, collaborative research, working alongside specialists in quantum algorithms, materials informatics, computational physics, chemistry, and condensed matter modeling. If you thrive in an intellectually stimulating environment and enjoy tackling fundamental and computational challenges in quantum materials science, we encourage you to apply.

This position requires a PhD in computational physics (or a closely related field), preferably with postdoctoral research experience (although postdoc experience is not mandatory). We are looking for a curious, creative, and interdisciplinary thinker with a comprehensive understanding of various computational physics, chemistry, and machine learning methodologies. The ideal candidate should be an avid reader of scientific literature, have expert-level hands-on coding experience (e.g., Python, Fortran, or C++), and possess deep expertise and skills in methodology development, which may include contributions to open-source atomistic simulation software packages. While prior knowledge of quantum information and fault-tolerant quantum computing is highly preferred, it is not required.

Responsibilities:

• Conduct theoretical and computational research in materials theory and simulations, advancing methodologies for modeling electronic structure, strongly correlated systems, and emergent quantum phenomena.
• Develop computational workflows that integrate established and emerging techniques, leveraging advances in high-performance computing (HPC), machine learning, and quantum computing.
• Collaborate with quantum algorithm developers, computational physicists, and materials scientists to explore new frontiers in quantum materials modeling.
• Contribute expertise in electronic structure theory and many-body physics to research efforts focused on understanding and predicting material properties.
• Stay at the forefront of developments in computational materials science, incorporating state-of-the-art approaches into research projects.
• Serve as a technical leader in interdisciplinary collaborations, working with internal and external teams on innovative solutions in quantum materials research.
• Engage in external partnerships, scientific discussions, and technical communication to support the broader research and application of advanced computational materials methodologies.
• Document research progress effectively and contribute to publications and external-facing materials that highlight advances in the field.

Experience/Qualifications:

Required:

• Ph.D. in theoretical or computational physics or chemistry or a closely related field, with a focus on electronic structure and many-body physics.
• Strong foundation in electronic structure theory, band structure calculations, and computational modeling.
• Expertise in methodology development within density functional theory (DFT).
• Proficiency in scientific programming and algorithm development, with experience in Python, Fortran, or C++.
• Familiarity with high-performance computing (HPC) environments and large-scale simulations.
• Enthusiasm for interdisciplinary collaboration, working across physics, chemistry, materials science, machine learning, and computational research teams.
• Strong peer-reviewed publication record.

Preferred:

• Experience with machine learning and data-driven approaches in computational modeling.
• Experience with quantum algorithms, particularly their application to materials modeling and electronic structure problems.
• Familiarity with wave function based computational chemistry methodologies, including coupled-cluster, configuration interaction (CI), and multi reference methods.
• Expertise in many-body and strongly correlated systems, including Fermi-Hubbard models, tensor networks, and embedding theories.
• Hands-on coding experience with Wannier function calculations.

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)

#LI-REMOTE

About this opportunity:

At Freenome, we are seeking a Senior Machine Learning Scientist to help grow the Machine Learning Science team, within the Computational Science department. The ideal candidate has a strong knowledge of artificial intelligence (AI), including machine learning (ML) fundamentals and extensive experience with deep learning (DL) methods, a track record of successfully using these methods to answer complex research questions, and the ability to thrive in a highly cross-functional environment.

They will be responsible for the development of algorithms for early, blood-based detection tests for cancer. They will build on a foundation of ML/DL and statistical skills to develop models for identifying molecular signals from blood. They will also work with computational biologists, molecular biologists and ML engineers to design and drive research experiments, and will have a significant impact on the continued growth of an organization dedicated to changing the entire landscape of cancer.

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

What you’ll do:

• Independently pursue cutting edge research in AI applied to biological problems (including cancer research, genomics, computational biology, immunology, etc.).
• Build new models or fine-tune existing models to identify biological changes resulting from disease.
• Build models that achieve high accuracy and that generalize robustly to new data.
• Apply contemporary interpretability techniques to provide a deeper understanding of the underlying signal identified by the model, ideally suggesting potential biological mechanisms.
• Work closely with ML Engineering partners to ensure that Freenome’s computational infrastructure supports optimal model training and iteration.
• Take a mindful, transparent, and humane approach to your work.

Must haves:

• PhD or equivalent research experience with an AI emphasis and in a relevant, quantitative field such as Computer Science, Statistics, Mathematics, Engineering, Computational Biology, or Bioinformatics.
• 3+ years of postdoc or post-PhD industry experience achieving impactful results using relevant modeling techniques.
• Expertise, demonstrated by research publications or industry achievements, in applied machine learning, deep learning and complex data modeling.
• Practical and theoretical understanding of fundamental ML models like generalized linear models, kernel machines, decision trees and forests, neural networks.
• Practical and theoretical understanding of DL models like large language models or other foundation models.
• Extensive experience with training paradigms like supervised learning, self-supervised learning, and contrastive learning.
• Proficient in current state of the art in ML/DL approaches in different domains, with an ability to envision their applications in biological data.
• Proficiency in a general-purpose programming language: Python, R, Java, C, C++, etc.
• Proficiency in one or more ML frameworks such as; Pytorch, Tensorflow and Jax; and ML platforms like Hugging Face.
• Experience in ML analysis and developer tools like TensorBoard, MLflow or Weights & Biases.
• Excellent ability to communicate across disciplines, work collaboratively, and make progress in smaller steps via experimental iterations.
• A passion for innovation and demonstrated initiative in tackling new areas of research.

Nice to haves:

• Deep domain-specific experience in computational biology, genomics, proteomics or a related field.
• Experience in building DL models for genomic data, with knowledge of state-of-the-art DNA foundation models.
• Experience in NGS data analysis and bioinformatic pipelines.
• Experience with containerized cloud computing environments such as Docker in GCP, Azure, or AWS.
• Experience in a production software engineering environment, including the use of automated regression testing, version control, and deployment systems.

Benefits and additional information:

The US target range of our base salary/hourly rate for new hires is $173,775 - $246,750. 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)

#LI-HYBRID

About this opportunity:

At Freenome, we are seeking a Staff Machine Learning Scientist to help grow the Machine Learning Science team, within the Computational Science department. The ideal candidate has a strong knowledge of artificial intelligence (AI), including machine learning (ML) fundamentals and extensive experience with deep learning (DL) methods, a track record of successfully using these methods to answer complex research questions, the ability to drive independent research and thrive in a highly cross-functional environment.

They will be responsible for the development of algorithms for early, blood-based detection tests for cancer. They will build on a foundation of ML/DL and statistical skills to develop models for identifying molecular signals from blood. They will also work with computational biologists, molecular biologists and ML engineers to design and drive research experiments, and will have a significant impact on the continued growth of an organization dedicated to changing the entire landscape of cancer.

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

What you’ll do:

• Independently pursue cutting edge research in AI applied to biological problems (including cancer research, genomics, computational biology, immunology, etc.).
  
• Build new models or fine-tune existing models to identify biological changes resulting from disease.
  
• Build models that achieve high accuracy and that generalize robustly to new data.
  
• Apply contemporary interpretability techniques to provide a deeper understanding of the underlying signal identified by the model, ideally suggesting potential biological mechanisms.
  
• Work closely with ML Engineering partners to ensure that Freenome’s computational infrastructure supports optimal model training and iteration.
  
• Take a mindful, transparent, and humane approach to your work.
  

Must haves:

• PhD or equivalent research experience with an AI emphasis and in a relevant, quantitative field such as Computer Science, Statistics, Mathematics, Engineering, Computational Biology, or Bioinformatics.
• 6+ years of postdoc or post-PhD industry experience achieving impactful results using relevant modeling techniques.
• Expertise demonstrated by research publications or industry achievements, in driving independent research in applied machine learning, deep learning and complex data modeling.
• Practical and theoretical understanding of fundamental ML models like generalized linear models, kernel machines, decision trees and forests, neural networks, boosting and model aggregation.
• Practical and theoretical understanding of DL models like large language models or other foundation models.
• Extensive experience with training paradigms like supervised learning, self-supervised learning, and contrastive learning.
• Proficient in current state of the art in ML/DL approaches in different domains, with an ability to envision their applications in biological data.
• Proficiency in a general-purpose programming language: Python, R, Java, C, C++, etc.
• Proficiency in one or more ML frameworks such as; Pytorch, Tensorflow and Jax; and ML platforms like Hugging Face.
• Experience in ML analysis and developer tools like TensorBoard, MLflow or Weights & Biases.
• Excellent ability to communicate across disciplines, work collaboratively, and make progress in smaller steps via experimental iterations.
• Proficient at productive cross-functional scientific communication and collaboration with software engineers and computational biologists.
• A passion for innovation and demonstrated initiative in tackling new areas of research.

Nice to haves:

• Deep domain-specific experience in computational biology, genomics, proteomics or a related field.
  
• Experience in building DL models for genomic data, with knowledge of state-of-the-art DNA foundation models.
  
• Experience in NGS data analysis and bioinformatic pipelines.
  
• Experience with containerized cloud computing environments such as Docker in GCP, Azure, or AWS.
  
• Experience in a production software engineering environment, including the use of automated regression testing, version control, and deployment systems.
  

Benefits and additional information:

The US target range of our base salary for new hires is $199,675.00 - $283,500.00. 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)

#LI-HYBRID

Role

As a Principal Mechanical Engineer at Vivodyne, you will be part of a multi-disciplinary team pushing the boundaries of automated, large-scale "organ-on-a-chip" robotics and related manufacturing systems. Your focus will be on developing and optimizing hardware and software that orchestrates robotic arms, embedded controllers, and microfluidic systems to enable high-throughput, high-precision biological workflows.

This role requires expertise across hardware and software, but primarily hardware.  From high-level motion control and machine vision to robotics programming and embedded firmware. You’ll work extensively with existing Python (less so with, C#, and C++) codebases, ensuring seamless system integration and performance.

You are a mechanical design expert.  Things like kinematic docking, 440 vs 316, FEA, and error budgets are very familiar to you.  Your friends include McMaster-Carr, Misumi, and Grainger. 

Collaboration is key—you’ll engage with mechanical engineers, biologists, immunologists, AI scientists, and process engineers to design and refine automation workflows that drive breakthroughs. We’re looking for a problem solver comfortable working across multiple domains, thriving in a fast-paced, high-innovation environment that values continuous learning and technical excellence.

This is a full-time, onsite role in San Francisco, where you’ll be designing, building, and testing some of the most advanced automation and high-throughput biology systems in the world.

Responsibilities

• Architect, design, and implement advanced automation systems, ranging from automated microfluidic manufacturing to high-precision mammalian cell culture platforms—all of which are mission-critical and highly visible both internally and externally.
• Develop hardware and software systems that acquire and process data reliably, ensuring smooth handoffs to production through rigorous documentation, data acquisition, and analysis.
• Embrace the dynamic nature of a rapidly growing startup, working across multiple projects and disciplines.
• Collaborate cross-functionally with Product leads, tissue engineers, microfluidics specialists, and AI researchers etc. to gather requirements, set technical objectives, and integrate new features into our robotics platforms.
• Design and implement resilience mechanisms to ensure system robustness, enabling fault detection, fault recovery, and minimal downtime, while maintaining data integrity.
• Proactively identify optimization opportunities, selecting priorities through engineering analysis and data-driven decision-making.
• Communicate progress and findings to internal stakeholders—including mechanical engineers, data scientists, and senior leadership—through data-laden presentations and technical reports.

Requirements and Expectations

Technical Expertise & Knowledge

• Bachelor’s degree (M.S. or Ph.D. preferred) in Mechanical Engineering, Chemical Engineering, or a related field with a focus on microfluidics, tissue engineering, or lab automation at least six years of directly relevant industry experience.
• Broad experience in hardware and, preferable, software design for automation, spanning off-the-shelf solutions and custom-built systems—you have developed complex automation workflows for production-level environments.
• Proficiency in key hardware and software tools, including SolidWorks (expert-level, CAM experience is a plus), and at some exposure to Python/Matlab/C++/C#/Javascript/etc.
• Hands-on experience with commonly used automation hardware, such as vision/camera systems (optics and/or optomechanical expertise is a plus), motion control, liquid handling, and pick-and-place robotics.
• Highly knowledgeable in various Manufacturing Technologies, you know how things are made and which approaches fit a particular application
• Industry-specific expertise is highly valued:
• Biotech/lab automation or semiconductor robotics experience is a strong advantage.
• Knowledge of microfluidics and fluid handling systems.
• Experience integrating robotics with high-content imaging and confocal microscopy systems, optimizing workflows for advanced imaging pipelines.
• Biology background or familiarity with cell and tissue engineering concepts is a significant plus, especially in handling biological samples.
• Bonus points for experience with:
• Electronics/PCBA design or basic knowledge (reading schematic, troubleshooting interconnects), statistical analysis/data collection to inform design performance rigorously

Problem Solving & Ownership

• You take an outcome-driven approach, translating project goals and technical challenges into systematic, resilient robotics solutions.
• Strong ownership mentality, ensuring the timely delivery of high-reliability features and improvements.
• Growth-oriented, continuously seeking ways to enhance automation processes, optimize robotic operations, and drive innovation.

Collaboration & Communication

• A team player, capable of working with engineers, scientists, and external stakeholders to define requirements, refine designs, and communicate project progress effectively.
• Clear communicator, able to explain complex robotics concepts to cross-functional teams, from software engineers to biologists.
• Interdisciplinary mindset, integrating feedback from various fields to develop thoughtful, iterative design improvements.

Innovation & Leadership Mindset

• Passionate about staying at the cutting edge of robotics, bringing fresh ideas from emerging technologies, best practices, and research to the team.
• Comfortable taking initiative in uncharted territories, whether it’s developing new motion-planning algorithms or integrating novel sensors and hardware.

Why Vivodyne?

• High-Impact Mission – Play a pivotal role in transforming drug discovery and human therapeutics through advanced robotics and automation.
• Multi-Disciplinary Exposure – Work alongside leading experts in robotics, AI, biology, and microfluidics, expanding your expertise in a truly collaborative setting.
• Growth Opportunities – A fast-growing startup environment with significant autonomy, ownership, and influence over best practices in the intersection of biotech and robotics.
• Innovative Culture – Join a team that prioritizes continuous learning, rigorous engineering and scientific standards, and bold problem-solving.

If you're excited about revolutionizing automation in organoid engineering and biotech—while pushing the boundaries of robotics, AI, and advanced systems—let’s connect! Please submit your resume and a brief statement of interest. We look forward to exploring the opportunity together.

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.