Full-Time Machine Learning Engineer
Job Description
High growth, pre-IPO company in stealth mode
Join a rapidly growing company that is developing ground-breaking genomics technologies that will push the boundaries in the clinical, life sciences research and therapeutic fields. We are well-funded by global top-tier investors, including venture capital firms and growth equity investors. Our team brings together unique and diverse expertise across multiple disciplines, from healthcare and life sciences, to engineering, to technology and software and beyond. We are a collaborative group of more than 200 employees, including successful entrepreneurs, hardware and software engineers, genomics and biotechnology experts, molecular and computational biologists, software and algorithm experts, and operations and commercial leaders. Play an important role in the development and commercialization of technology and products that will accelerate our understanding of life, biology and disease and transform large industries.
We are looking for a highly motivated Machine Learning Engineer to join our team!
You will be responsible for monitoring the performance of complex scientific instruments. As our Machine Learning Engineer, you will build a cloud-based program to associate out-of-spec instrument characterization metrics with physical or process root causes. You will monitor these characterization metrics across a fleet of instruments, flag instrument misbehavior in near-real time, and indicate underlying problems to be addressed.
How You’ll Contribute
- Maintain database of characterization metrics.
- Collaborate closely with data scientists and system engineers.
- Write and maintain production-quality, well-documented code.
- Flag instrument misbehavior in near-real time and predict underlying root cause.
- Interface with data analysis pipeline team to ensure that your code runs reliably.
- Display instrument monitoring data on built-in UI.
- Collaborate across teams on priorities, deadlines, and deliverables.
Qualifications, Skills, Knowledge & Abilities
- Bachelor’s degree in a quantitative field.
- Formal training in Software Engineering.
- Experience diagnosing complex instruments.
- 5 years hands-on machine learning experience.
- Experience with cloud-based applications.
- Proficient with supervised machine learning.
- Experience with near-real time monitoring programs
- Data visualization experience.
- Hands-on experience with NoSQL databases.
- Excellent written and verbal communication skills.
- Familiarity with molecular biology concepts is preferred.
- Experience with complex scientific instrumentation is preferred.
221 total views, 0 today