Freenome

Freenome

Senior Machine Learning Engineer

Brisbane, California · Senior

Sponsorship not specified$162k-$227kDetected 7 days ago
PythonJavaC++Distributed SystemsGitAWSGCPAzureCloud PlatformsDockerKubernetesCI/CDMachine LearningDeep LearningTensorFlowPyTorchSparkLLMsMLOpsStatisticsComplianceControlsBioinformaticsResearch

About the role

  • 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.
  • We invite you to check out our career page @ freenome.com/job-openings/ for additional company information.

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.
  • 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.
  • 5+ years of post-MS industry experience working on developing AI/ML software engineering pipelines.
  • 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.
  • 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.
  • Expertise in building and launching large-scale ML frameworks in a scientific environment that supports the needs of a research team.

Requirements

  • 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.
  • 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.

Nice to have

  • 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.
  • 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.
  • Excellent ability to work effectively with cross-functional teams and communicate across disciplines.
  • Proficiency in a general-purpose programming language: Python (preferred), Java, Julia, C, C++, etc.

Compensation

  • 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.

Benefits

  • Family & Medical Leave Act (FMLA)
  • 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.
  • Strong knowledge of ML and DL fundamentals and hands-on experience with machine learning frameworks such as PyTorch, TensorFlow, Jax or Scikit-learn.
  • Benefits and additional information:

Equal opportunity

  • Equal Employment Opportunity (EEO)
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This listing is sourced directly from Freenome's careers page and normalized into a canonical job model.