The Role
About PackageX:
PackageX automates data entry and manual logistics processes for receiving, inventory, and fulfilment in buildings, warehouses, and stores. It uses advanced AI scanning, flexible bolt-on apps, and APIs to drive exceptional workforce productivity, fulfilment efficiency, and real-time visibility.
Our vision is to build the most advanced logistics infrastructure company that orchestrates the movement of physical things and becomes the defining backbone of the digital supply chain.
We're a fast-growing pre-Series A stage startup in New York City with a distributed global team backed by Bullpen Capital, Pritzker Group, Sierra Ventures, Ludlow Ventures, MXV Capital, and NSV Wolf Capital.
About the Role:
We are looking for a passionate and experienced Machine Learning Engineer with a strong background in Computer Vision, Natural Language Processing (NLP), and Large Language Models (LLMs) to join our growing AI team. The ideal candidate is someone who stays up to date with the rapidly evolving ML ecosystem, has hands-on experience solving real-world problems, and can take ownership of end-to-end ML solutions — from research to deployment.
Key Responsibilities:
· Design, build, and deploy machine learning models, particularly in the areas of computer vision and NLP.
· Develop and fine-tune large language models (LLMs) for specialized use cases.
· Translate business and product challenges into ML solutions.
· Collaborate closely with product, design, and engineering teams to integrate ML models into real-world applications.
· Research and implement state-of-the-art techniques while staying ahead of emerging trends in ML and AI.
· Optimize ML models for deployment on mobile and edge devices (Android/iOS).
· Analyze and improve model performance through rigorous testing and evaluation.
· Document findings, contribute to knowledge sharing, and mentor junior engineers if needed..
Required Skills & Qualifications:
· 5+ years of hands-on experience in machine learning.
· Strong foundation in Computer Vision (e.g., image classification, object detection, OCR) and NLP (e.g., transformers, text generation, embeddings).
· Experience working with Large Language Models (OpenAI, Hugging Face, LLaMA, etc.).
· Solid understanding of ML frameworks such as TensorFlow, PyTorch, or JAX.
· Experience deploying ML models in production environments.
· Proven track record of solving real-world business or technical problems using machine learning.
· Familiarity with optimizing ML models for mobile and edge devices is a strong plus.
· Proficient in Python and ML tooling (e.g., NumPy, Pandas, scikit-learn, Hugging Face, OpenCV).
· Strong analytical and problem-solving skills.
Nice to have skills:
· A strong plus if you have worked on deploying ML models on mobile platforms.
· Exposure to MLOps pipelines and tools (e.g., MLflow, Weights & Biases).
· Contributions to open-source projects or technical blogs.
· Master's or PhD in Computer Science, AI, Machine Learning, or related field.
What can you expect from the application process?
All applications will be looked at by the People team who will reach out to shortlisted candidates. Across various interview rounds, you'll speak with the hiring manager and other functional heads. We want to have an open discussion about your work and how we can be a great fit for each other. The process may also involve an assessment or presentation relevant to the role. You can expect an offer after three rounds of interviews. All offers are subject to satisfactory reference and background checks.
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The Company
What We Do
PackageX is an enterprise logistics platform that powers modern logistics operations in warehouses, stores, and final-mile buildings. Leveraging advanced AI scanning, bolt-on apps, and APIs, PackageX automates data entry and manual logistics processes, maximizes workforce productivity, and enhances operational visibility. Trusted by businesses worldwide, including CBRE, Hilton Grand Vacations, On Running, Ricoh, and Two Boxes, PackageX is transforming logistics workflows for a faster, more connected world
