Machine Learning Engineer

Overview

Arrive Logistics is a leading transportation and technology company in North America, with plans to continue to significantly grow year over year. Our success is a testament to our remarkable team and what we are building together. We’re committed to providing employees with a meaningful work experience and have established an award-winning culture that supports personal and career development in a fun, casual, and collaborative environment.

There has never been a more exciting time to get on board, so read on to learn more and apply today!

Machine Learning Engineer

Arrive Logistics

Austin, TX (On-site, Remote)

Full-time

17 hours ago

• Design, build, and maintain scalable ML systems and infrastructure using Python, Postgres, and Elasticsearch.
• Partner closely with other Machine Learning Engineers, Product Managers, Data Scientists, Data Engineers, and Product Engineers to ensure the successful delivery of strategic and roadmap initiatives
• Own systems throughout the software development lifecycle, from design to development, deployment and monitoring.
• Maintain and improve performance of existing data systems and processes while balancing maintainability, observability and readability.
• Participate in an on-call rotation where you will support incidents and questions about service behavior from product managers.
• Demonstrate a deep sense of ownership by developing a thorough understanding of a domain. At the same time, you must be able to explain the behavior of and contribute to code bases that may be outside your domain.
• Proactively propose solutions to gaps in process, technology, software design and architecture
• Provide rigorous and detailed code reviews that uphold team standards, testing and software design best practices
• Foster a culture of constant improvement and growth, engineering excellence, humility, positivity and curiosity. Take a lead role in making our two days in the office productive and engaging, fostering face-to-face mentorship and collaborative whiteboarding sessions.

• Bachelor’s degree in Computer Science, Engineering, or a related field or equivalent professional experience.
• 3+ years of experience with ML ops (features stores, model registries and auto ML), model serving and optimization.
• 3+ years of experience with Python and building highly scalable backend services
• Experience with frameworks like Sklearn, Pandas and Numpy. Plus if Huggingface, Tensorflow, Pytorch experience.
• 2+ years of experience with relational databases
• 2+ years of experience designing maintainable and scalable systems
• System design experience with a focus on loosely coupled, distributed systems, microservices and event-driven architectures
• Experience developing cloud-native dockerized applications in Kubernetes
• Experience working with online experimentation and platforms like Statsig
• Understanding of both traditional machine learning and deep neural networks
• Strong communication skills with the ability to articulate, diagram and document complex ML or engineering concepts.
• Strong analytical, problem-solving, decision-making, and interpersonal skills.
• Strong project management and organizational skills with experience identifying project milestones to ensure timely project delivery
• You are a self-starter who can deliver projects independently, yet you also thrive in collaborative environments. You recognize the value of diverse perspectives in developing optimal solutions and consistently demonstrate a willingness to support colleagues as a strong team player.
• You approach software engineering as a craft, balancing the pursuit of clean, maintainable code with the demands of a fast-moving, dynamic business environment. You collaborate effectively with product managers and leadership to choose development paths that minimize technical debt while ensuring the timely delivery of high-quality products. While you have a strong drive for innovation, you also recognize the critical need to stabilize and harden existing products and services.
• You believe that while remote work is functional, in-person collaboration is where the "magic" happens. You are excited…