Lead Machine Learning Engineer

About Cerebre

Cerebre is a rapidly growing global team on a mission to digitize the physical world. Our software transforms traditional sources of engineering knowledge into accessible, usable data that supports field operations and advanced analytics.

Our team is developing cutting-edge technology to make our physical world safer, more productive, and environmentally sustainable.

We collaborate with the largest companies and most innovative clients and partners who aim to transform the industrial industry.

Our development team is comprised of world-class engineers who design and create novel solutions.

This is an opportunity to join a market-leading team with opportunities to change the industrial world.

If you love building and creating value in the "white space," if you love freedom and flexibility to think outside the box, if you are passionate about working with critical thinkers who challenge the status quo, and if you aspire to work in a fast-paced environment, we would love to get to know you!

We believe flexibility leads to creativity and that our team should live and work where we are our best selves. We are 100% remote with flexible PTO and unlimited mental health days.



About the role

We are looking for a Senior Machine Learning Engineer to take our platform to the next level. As a technology team member, you will work daily with our world-class engineers. You will play a pivotal role in developing and implementing advanced machine learning solutions from scratch to enhance the efficiency and experience we provide to our clients. You will work closely with a highly technical team of engineers to design, build, and validate a series of machine learning models and manage an ML pipeline.

If you are interested in solving challenging technical problems with the freedom to think creatively, this role is for you.



Required Skills:
- Bachelor of Science in Computer Science, Computer Engineering, Data Science, or a related field. Relevant hands-on experience will be considered in lieu of (or in addition to) a degree.
- Fluent in Python
- 3-5 years of experience and/or past projects with ML-based software development, including building, developing, and deploying machine learning models
- Experience and/or past projects with image recognition, transfer learning approaches, and/or graph machine learning
- Experience in building ML pipelines (MLOps)
- Experience and/or past projects using Docker or other containerization software
- Ability to produce models as ready-to-use container images with APIs
- Experience using Git
- Familiar with TensorFlow or PyTorch
- Familiar with current state-of-the-art models and able to apply them in the proper context
- A track record of successful collaboration on development teams
- A critical thinker with strong problem-solving skills and the ability to communicate and collaborate effectively with the rest of the team
- Commitment, resourcefulness, and the ability to balance multiple projects


Preferred Skills:
- Experience, past projects and/or familiarity with:
- Python typing (mypy) and Python-scaling approaches (Ray, pySpark)
- LabelStudio
- OpenCV library
- Data engineering tools such as Dagster, Prefect, Spark
- Kubernetes and Kubeflow
- C#/ML.NET
- Linux/Unix tools and fluent in terminal
- Familiarity with CAD drawings
- Experience completing work with basic engineering tasks and standards such as writing tests, code reviews, merge request-based development (feature branches), and CI/CD pipelines
- Engineering background beyond software (i.e., mechanical, electrical, nuclear, etc.)
- Previous startup experience


More about Cerebre:
We are cross-functional collaborators.
We blend manufacturing process knowledge with software and big data engineering expertise to create value in physical settings

We are experienced.
We are armed with industry-leading experts in numerical simulation, combustion, power, computational fluid dynamics, and chemical process modeling

We are serious builders.
We develop our platforms using leading practices in IT/OT architecture, OT security, AI architecture, ML Ops, and Platform engineering