Who we are: We are industry veterans and data-scientists using innovative technology to fearlessly reinvent the future of freight. As the ‘nerds of logistics’, we seek intelligence in data to solve deep rooted inefficiencies in the industry. We give shippers, brokers and carriers access to our data connections that link supply and demand and a suite of award-winning solutions to strike the perfect balance of cost and service. We’re creating a more efficient and environmentally responsible way to move more with less.
Who you are: You believe in game-changing innovations and are excited about reimaging a 700 billion dollar industry. You know how to take ideas, optimize them, and push it into production code using Python. You have experience putting your machine learning models in production, and know your way around databases and ETLs. You like working in a hands-on environment and enjoy teamwork.
Where we are: Loadsmart was founded in New York and is currently headquartered in Chicago, IL. Our teams operate remotely from different parts of the United States as well as in several locations across Latin America.
The role: We are looking for a Sr. Machine Learning Engineer to work remotely from Lat-Am. You’ll join us in obsessing about transformational technology as part of one of our Data Science teams. You should have experience and proven ability to analyze data, create machine learning models, implement APIs to serve these models, and write ETLs and data pipelines to push these models to production. Our technology stack on the backend is primarily on top of Docker, Kubernetes and Python.
Because we are an international company, we only accept resumes in English. At Loadsmart, we believe our biggest asset is our people. We are proud to be an equal opportunity employer, hiring and developing individuals from diverse backgrounds and experiences to add to our collaborative culture. Loadsmart treats all candidates and employees with respect and does not discriminate in our recruiting, hiring, and promoting processes, including on the basis of race, color, religion, sex, age, sexual orientation, gender identity and/or expression, national origin, veteran status, or disability.