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

Role

The AI Team at Vivodyne tackles some of the hardest and most interesting challenges in science and engineering. With access to extraordinarily feature-rich and massive-scale Vivodyne human tissue imagery, we are advancing the frontiers of artificial intelligence and its applications in biology. We are building a portfolio of AI technologies to automate the discovery, development, and de-risking of novel therapies using our unique technology platform, including single-cell 3D phenomics/machine vision, multimodal (multi-omic) translation, and reinforcement learning for robotic planning & study design, among others.

As an AI Senior Scientist, you will leverage your deep expertise in developing large-scale models (including Transformer, Diffusion, and hybrid architectures) and Generative AI solutions to help turn our state-of-the-art imaging and multi-omics datasets into groundbreaking scientific insights. You will collaborate closely with biologists, engineers, and AI specialists to deliver robust, production-ready algorithms and models that power Vivodyne’s high-impact discoveries.

This role will be based on-site at our offices in Brisbane, California

Responsibilities

• Scientific Innovation — Work closely with internal and external stakeholders to understand their scientific objectives and innovate new approaches that utilize Vivodyne’s massive-scale imaging and multi-omics datasets.

• Model Development — Lead hands-on development of cutting-edge machine learning models (Transformers, Diffusion, hybrid architectures, etc.) with a focus on image processing, image enhancement, and Phenomap embeddings.

• Data Pipeline Optimization — Build and deploy pipelines capable of scaling to petabyte-scale data, ensuring robust MLOps practices on AWS or equivalent cloud platforms.

• Multimodal Integration — Explore and develop novel methods to incorporate multi-omics data into imaging-based Phenomaps, advancing the state of the art in multimodal phenotypic analysis.

• Collaboration & Communication — Partner with tissue engineers, microfluidics experts, and robotics engineers to refine data collection strategies, provide feedback on imaging system performance, and identify opportunities for improved AI-driven solutions.

• Scientific Rigor & Leadership — Uphold scientific excellence through peer reviews, proper documentation, and clear communication. Drive a culture of continuous improvement, best practices, and adherence to rigorous research methodologies.

• Scalability & Efficiency — Emphasize modularity, composability, and performance efficiency in designing and implementing high-throughput compute and analytics pipelines.

• Domain & Technical Growth — Remain current with the latest research trends in Generative AI, biomedical imaging, cloud computing, and data-intensive training. Proactively share knowledge and mentor teammates to foster overall organizational expertise.

Requirements and Expectations

• Scientific Excellence — Stay current with AI/ML research, especially in Generative AI, Transformers, Diffusion models, and multi-modal architectures. Apply rigorous, data-driven methods for sound outcomes.
• Hands-On Leadership — Set high standards for model development and code quality, mentoring team members and fostering innovation.
• Accountability — Own model and infrastructure development from concept to deployment, delivering reliable, scalable solutions aligned with Vivodyne’s mission.
• Problem Solving & Adaptability — Develop creative solutions to novel research challenges, thriving in a fast-paced, dynamic startup environment.
• Collaboration & Communication — Work cross-functionally to align AI strategies with business goals, ensuring clear communication and consensus-building.
• Delivering Results — Prioritize impactful research, using project management best practices to track progress, mitigate risks, and meet deadlines.
• Architecture & Coding — Design scalable, cost-effective systems and produce clean, well-documented code with continuous testing and governance compliance.
• Resource Optimization — Apply financial discipline to maximize the efficiency of compute, storage, and third-party services.
• Team & Thought Leadership — Foster an inclusive environment, mentoring future leaders and representing Vivodyne in AI research and industry discussions.

Qualifications

• Education & Experience

• PhD in Computer Science, Applied Mathematics, or a related field, or equivalent practical experience.
• Proven expertise developing and deploying advanced ML architectures (Transformers, Diffusion, multi-modal models) in Generative AI settings.
• Demonstrated history of success with image processing and large-scale model training.

• Technical Skills

• Strong knowledge of AWS or another major cloud provider for MLOps, big data processing, and scalable computing.
• Experience building models for petabyte-scale datasets and understanding the associated architectural and operational complexities.

• Domain Expertise (Preferred)

• Familiarity with biological or biomedical imaging; advanced knowledge in multi-modal Phenomaps, especially integrating imagery with multi-omics data, is a major plus.

• Additional Desired Skills

• Prior experience with HPC, GPU clusters, or distributed computing frameworks.
• Comfortable with container orchestration and open-source data engineering/ML frameworks.

• Soft Skills & Leadership

• Strong written and oral communication skills, with a proven ability to present complex technical topics to non-experts.
• Demonstrated ability to thrive in ambiguous environments, driving clarity and direction with minimal supervision.

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.