Machine Learning Engineer at Cartesian in Cambridge, MA

Job description

• We're looking for a highly motivated, product-oriented Senior Machine Learning Engineer to join our core R&D team at a pivotal moment in our growth. You'll own ML problems end to end, from framing and data, through modeling and evaluation, to production and monitoring across hundreds of live stores, and help shape the technical roadmap of a category-defining product. We move fast, care deeply about quality, and value people who take initiative and crave real-world impact.
• Because our challenges span wireless signals, time series, spatial reasoning, research and production, success in this role requires a unique balance: the breadth to connect the dots across diverse domains, and the depth of judgment to evaluate trade-offs rigorously, dig into the details when models fail, and confidently drive solutions to production; we want an adaptable engineer who can navigate ambiguity with high technical standards.
• You'll be joining us in-person in the heart of Kendall Square, Cambridge, next to MIT and the Charles River.
• We’re a fast-moving MIT startup at an important inflection point for our product growth and direction. We are building a talent-dense team of engineers and applied researchers to solve hard, high-impact problems in retail operations.
• You will have outsized ownership and autonomy. You will grow extremely quickly and make important contributions to our product, engineering culture, and company direction. We will push you to become a better engineer, and we will expect the same from you.

Responsibilities

• Work closely with applied scientists to design, develop, deploy, and monitor deep learning and ML models for indoor positioning and item localization, owning problems end-to-end.
• Build and improve the training, inference, and evaluation pipelines that take models from prototype to production.
• Optimize models and systems for accuracy, coverage, latency, and cost, and own the trade-offs between them.
• Develop tools and datasets to benchmark performance in real-world, at-scale settings.
• Collaborate with engineering and product teams to prioritize what's worth building and to ship features to enterprise customers.
• Raise the team's engineering bar through reusable infrastructure, better design patterns, and honest technical judgment on architecture and technology choices.

Requirements

• BSc/MSc in computer science, electrical engineering, or related field with 5+ years of applied ML experience, with at least one system taken personally from ambiguous problem to shipped, measured impact.
• Broad, hands-on command of machine learning (e.g. supervised and unsupervised learning, embeddings and representations, metrics and evaluation) with strong practical judgment on overfitting, generalization, and competing objectives.
• Strong software engineering fundamentals and the ability to write high-quality production code (we work primarily in Python/PyTorch).
• Strong data instincts: comfortable digging into raw data, questioning metrics, and validating your own results.
• Excellent communication skills and ability to collaborate across disciplines.
• Thrive in fast-paced, dynamic environments and take pride in producing high-quality work.
• Deep expertise in a relevant subfield, e.g., time-series models (transformer-based or probabilistic/state estimation), representation learning, computer vision, or multi-sensor fusion (2D/3D perception, pose estimation, tracking, SLAM).
• Background in wireless localization, RFID, or radar signal processing.
• Experience optimizing and deploying ML models in mobile or resource-constrained environments.
• Familiarity with cloud-based model training and inference.
• Research experience, advanced degree, or publications in ML, vision, or systems venues.
• Past startup experience.

Skills

Required

• Python/PyTorch
• Software engineering fundamentals
• Data analysis and validation
• Communication and collaboration

Preferred

• Representation learning
• Computer vision
• Cloud-based model training and inference
• Research experience or publications