Bearing - Machine Learning Engineer

Global /
AI Fund – /
Full time
About Bearing AI:
Bearing is at the forefront of bringing AI to the maritime shipping industry. This is a trillion dollar industry that moves 90% of the goods we interact with on a daily basis, but has traditionally lagged far behind other industries in adopting new technologies. At Bearing, we’re changing that. We’re building an AI-driven platform that helps shipping companies dramatically streamline their operations and tackle their single biggest pain point. We’ve already built our first product and have some of the world’s biggest shippers as our partners. Our team is made up of highly experienced AI engineers and tech industry veterans and we’re backed by the AI Fund and shipping industry investors.

What You Will be Doing:
We are currently looking for an exceptionally talented Machine Learning Engineer to join our small team. You will be responsible for end-to-end ownership of scalable Machine Learning systems — from data pipelines, to training, to analyzing performance in a production environment. Since you’ll be joining an early-stage startup at the ground level, you’ll need to be able to wear multiple hats and thrive while working in a dynamic environment. 

Design and train new production-ready machine learning models. You will lead the development of new machine learning models focused on converting a wide variety of data sets (e.g., ship sensors, weather, market rates) into actionable insights. You will apply fundamental machine learning concepts to quickly iterate and debug model related issues and develop new techniques to handle unique cases with each customer. 

Collect, process and analyze data. A big part of our machine learning projects is understanding and analyzing the data. You must build pipelines and processes for cleaning and organizing data as well as build tools to help analyze data. You must also understand what types of data models are struggling with and use this analysis to propose solutions.

Analyze and improve existing models. You will also be responsible for analyzing performance of our existing models and work to improve their accuracy by applying the latest published research, feature engineering and tuning of hyperparameters.

What You Must Bring:

Must Haves:
3+ years professional experience designing, training and deploying machine learning models
Strong computer science foundation including data structures, algorithms and design patterns
Expertise in Python demonstrated by implementing multiple medium to large scale projects
Proven ability to implement and debug machine learning models
Excellent communication skills and the ability to have in-depth technical discussions with both the engineering team and business people
Familiarity with machine learning frameworks and libraries (e.g., scikit-learn, Keras, TensorFlow, PyTorch)
Industry experience with relational databases and SQL-based tools
BSc in Computer Science, Mathematics or similar field; Master’s or PhD degree is a plus
Self-starter and comfort working in an early-stage environment

Nice to Haves:
Experience with big data pipeline technologies such as MapReduce, Spark, Kafka
Research experience in machine learning or artificial intelligence related field
Contributions to open source ML projects
Experience working on logistics or shipping-related products
Experience with Agile development 
At AI Fund, we are committed to providing an environment of mutual respect where equal employment opportunities are available to all applicants without regard to race, color, religion, sex, pregnancy (including childbirth, lactation and related medical conditions), national origin, age, physical and mental disability, marital status, sexual orientation, gender identity, gender expression, genetic information (including characteristics and testing), military and veteran status, and any other characteristic protected by applicable law. AI Fund believes that diversity and inclusion among our employees is critical to our success as a company, and we seek to recruit, develop and retain the most talented people from a diverse candidate pool. Selection for employment is decided on the basis of qualifications, merit, and business need.