Freenome · 9 hours ago
Senior Machine Learning Engineer
Brisbane, CA
Full-time
Hybrid
Senior Level
$162K/yr - $247K/yr
5+ years expFreenome is seeking a Senior Machine Learning Research Engineer to join their Machine Learning Science team within the Computational Science department. The role involves developing and deploying infrastructure for deep learning models using genomic data, collaborating with interdisciplinary teams to optimize machine learning pipelines and contribute to early cancer detection initiatives.
BiotechnologyHealth CareHealth DiagnosticsOncologyPersonal Health
Responsibilities
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
Qualification
Required
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
Preferred
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
Equity
Cash bonuses
Full range of medical, financial, and other benefits
Company
Freenome
Freenome is a biotechnology company developing blood tests for early cancer detection to improve access to routine screening.
201-500 employees
H1B Sponsorship
Freenome has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
reference. (Data Powered by US Department of Labor)
Distribution of Different Job Fields Receiving Sponsorship
Represents job field similar to this job
Trends of Total Sponsorships
2025 (7)
2024 (11)
2023 (15)
2022 (10)
2021 (7)
2020 (7)
Funding
Current Stage
Late StageTotal Funding
$1.53BKey Investors
RocheExact SciencesS32
2025-12-04Acquired
2025-11-17Corporate Round· $75M
2025-08-06Corporate Round· $50M
Leadership Team
Aaron Elliott
Chief Executive Officer
Riley Ennis
Co-Founder
Recent News
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