Machine Learning Engineer
Menlo Park, CA
Introduction to Aether
Aether was founded under the belief that synthetic biology will fundamentally change the future of manufacturing. In order to catalyze the next industrial revolution, we are building a fully automated robotic laboratory that will generate enough data to leverage deep learning for biological engineering for the very first time. To do this, we are building a diverse team of software engineers, machine learning engineers, process engineers, roboticists, bioengineers, environmentalists, sci-fi nerds, and world-changers. We hope you can join us.
Aether believes the key to unlocking the potential of bioengineering and nanotechnology is the application of deep learning algorithms trained on massive biological datasets. To leverage the data our high-throughput laboratories will generate, Aether requires sophisticated, scalable, and high-performance deep learning models. These models will define our ability to engineer biology and transition humanity to nanotechnology-based manufacturing, and we are looking for the best and brightest to join us in this challenge.
As an early member of our machine learning team, your passion and ideas will play a significant role in defining the infrastructure that will serve as the foundation for our deep learning efforts. You will be developing software from the ground-up with an early-stage team, and as such we are searching for someone who is excited by the autonomy, creative freedom, and rigor that this opportunity represents.
As a machine learning engineer you will design, build, and improve the systems responsible for the development and deployment of our machine learning models. By understanding the memory, storage, and compute requirements of our models, you will design and implement how our data is stored and transferred, how our models are trained, and how our models perform inference. You will be responsible for automating the processes of deploying our workflows, monitoring the health of our nodes and pipelines, repairing and/or redeploying corrupted workflows, and all other efforts related to workflow management. To accomplish these tasks you will be designing our machine learning infrastructure from the ground up, making key decisions such as whether to use cloud vs on-premise compute/storage, which tools should be built vs integrated from pre-built solutions, etc. The goal of these efforts will be to build a robust, scalable, and cost-efficient infrastructure optimized for speed and ease-of-use.
In addition, you will work closely with the machine learning science team to build tools and systems that enable them best in their effort to design, training, and evaluate models. Similarly, you will work closely with the software engineer/devops teams to form a bridge between laboratory information management systems (LIMS) and our machine learning systems.
Lastly, you will help grow a team of innovative, high-performing machine learning engineers and play a critical role in defining the technical environment in which the machine learning team executes. Enabling a nanotechnology revolution requires a cutting-edge deep learning platform that is unmatched in its predictive performance, speed, scalability, and extensibility, and you believe you are up to this task.
As a member of the technical staff within Aether, you will play a key role in shaping our technical culture, building world class technology solutions, and evangelizing the use of technology across the organization. Core responsibilities include:
Building robust, scalable systems for:
- Data storage and transfer
- Model training
- Model inference
Automating processes for:
- Workflow deployment
- Monitoring health of nodes and pipelines
- Repairing and/or redeploying corrupted workflows
Make key strategic decisions on cloud vs on-premise compute/storage, which tools to build vs integrate from pre-built solutions, etc.
Build tools enabling the machine learning science team
Build the bridge connecting our laboratory information management systems (LIMS) and our machine learning systems
Perform other related duties as assigned and based on Company needs
- Bachelor's degree in Computer Science, Mathematical Computing, Data Science, Bioinformatics, Machine Learning, or other relevant field
- At least 2 years of industry experience building state-of-the-art software infrastructure
- Demonstrated ability to build scalable production systems that leverage cloud computing (i.e. AWS or GCP)
- Experience with git and version control
- Fluent in Python and PEP 8
- MS or PhD in Computer Science, Mathematical Computing, Data Science, Bioinformatics, Machine Learning or other relevant field
- At least 5 years of industry experience building state-of-the-art software infrastructure
- Experience building infrastructure for deep learning systems
- Basic to expert knowledge of core deep learning concepts
- Experience in rapidly growing start-ups
- People management / hiring experience