Want to learn more about this role and Jobot? Click our Jobot logo and follow our LinkedIn page! Job details Exciting Senior Machine Learning Engineer role with rapidly growing Biotech on the cutting edge of protein design! This Jobot Job is hosted by Coalter Powers Are you a fit? Easy Apply now by clicking the "Easy Apply" button and sending us your resume. Salary $125,000 - $175,000 per year A Bit About Us • ***REMOTE OR HYBRID**** Our client, a cutting-edge biotech company recently announced a large partnership with Google Cloud to build a generative AI platform for engineering biology and for biosecurity. We are exclusively partnering with them on the build-out of the newly created Digital Tech / AI Enablement team. This is an excellent opportunity to get in early and be a part of it all. Why join us? The AI Enablement team is responsible for delivering the ML expertise required to make this happen. With nearly limitless compute capacity CPU, GPU, or TPU; you will have the opportunity to partner with biologists, software and DevOps engineers, and data scientists to create the necessary ML infrastructure. As one of the first members of this new team, you will have a large role in molding our approaches to creating foundation models for biology, as well as creating fine-tuned and derived models for specific applications in bioengineering. • Work remotely, hybrid, or in-office • Competitive Compensation including industry leading equity program • Nearly limitless AI/ML resources and capacity • Unlimited opportunity for career development and growth Job Details While the main focus of your work will be on building and evaluating new ML models for biology, many other types of work will come your way. You may need to do data archaeology, create and debug pipelines in tools like Kubeflow or Flyte, quickly learn the basics of protein folding or codon optimization, become the company’s expert on a new tool, debug odd results created by a production model for a project under a time crunch, contribute to brainstorming, planning and prioritization, make presentations, give feedback on others’ proposals and code, and more. This is a new team, a significant company focus, and a rapidly evolving field. You will need to be able to handle ambiguity and uncertainty; on the flip side, you will be able to influence where things go and how they change. You will identify what needs to happen, bring it to the team’s attention, and make it happen. You will not be expected to be an expert in “All The Things”. You will be expected to have a high level of general technical competency, be a fast learner brimming with curiosity, and an expert in a few things - deep learning, in particular. Responsibilities • Build, manage, and evolve a GCP-based platform for large scale (up to 100B+ parameters) training, evaluation, and serving of Foundation Models for biology. • Develop, implement and maintain a system for creating smaller models that combine large FMs with additional experimental data to address specific needs and applications. • Own processes for data ingestion, data prep, data and model provenance tracking, and various other data engineering and ML Ops activities. • Contribute to model design and experimentation. • Identify opportunities for application of AI and ML across the company, create prototypes, and contribute to overall prioritization and roadmap development for AI. Minimum Requirements • PhD in a scientific discipline and a minimum of 5 years related experience; may include post-doctoral experience; Masters and 7 years of related experience; Bachelors and 9 years of related experience in data engineering, systems engineering, machine learning and operations, MLOps, or similar roles; or equivalent industry experience. • Deep experience with Python. • Experience with ML and data orchestration and workflow engines like Airflow, Kubeflow, Flyte, or Dagster. • Familiarity with recent literature and state of the art for large model architectures and training approaches • Experience with building machine/deep learning models with at least one common framework such as PyTorch, Tensorflow, or JAX. Preferred Capabilities And Experience • Practical experience iterating on LLM design • Familiarity with the ML ecosystem, including MLFlow and related tools • Experience with Terraform or Pulumi, Kubernetes • Experience operating non-trivial GCP deployments • Experience with Vertex AI services • Experience with “Cloud Life Sciences” / Google Batch Interested in hearing more? Easy Apply now by clicking the "Easy Apply" button. Want to learn more about this role and Jobot? Click our Jobot logo and follow our LinkedIn page