Machine Learning Engineer
Cartesiansystems · Cambridge, MA
About this role
**Machine Learning Engineer** **About the Company** Cartesian is building spatial intelligence for indoor environments to drive operational efficiency—helping retailers improve in-store inventory visibility with accurate indoor positioning and actionable product location insights. Their infrastructure-free platform fuses wireless signals and mobile computer vision and is already deployed by international fashion brands. **About the Role** We’re seeking a **Senior Machine Learning Engineer** to join our core R&D team. 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. You’ll also help shape the technical roadmap for a category-defining product. **Responsibilities** - Work with applied scientists to design, develop, deploy, and monitor deep learning/ML models for indoor positioning and item localization (end-to-end ownership) - Build and improve training, inference, and evaluation pipelines from prototype to production - Optimize models and systems for accuracy, coverage, latency, and cost—owning trade-offs - Develop tools and datasets to benchmark performance in real-world, at-scale settings - Collaborate with engineering and product teams to prioritize and ship features to enterprise customers - Raise the team’s engineering bar with reusable infrastructure, better design patterns, and strong technical judgment **Qualifications** - BSc/MSc in CS, EE, or related field; **5+ years** of applied ML experience - At least one system taken from ambiguous problem to shipped, measured impact - Broad, hands-on ML expertise (e.g., supervised/unsupervised learning, embeddings/representations, metrics/evaluation) with strong judgment on overfitting/generalization and competing objectives - Strong software engineering fundamentals; production-quality code (primarily **Python/PyTorch**) - Strong data instincts: comfortable digging into raw data, questioning metrics, and validating results - Excellent communication and cross-disciplinary collaboration - Thrives in fast-paced environments and takes pride in high-quality work **Nice to Have** - Expertise in time-series models, representation learning, computer vision, or multi-sensor fusion (e.g., pose estimation, tracking, SLAM) - Background in wireless localization, RFID, or radar signal processing - Experience deploying ML in mobile/resource-constrained environments - Familiarity with cloud-based training and inference - Research experience, advanced degree, or publications - Startup experience **Why Now** This is an important inflection point for product growth and direction. You’ll have outsized ownership and autonomy, grow quickly, and make meaningful contributions to the product, engineering culture, and company direction. **Technology** - **Backend:** FastAPI, Python - **Data:** Postgres, Blob Storage, Parquet - **Machine Learning:** PyTorch, Ray - **Infrastructure:** Azure, Kubernetes, Helm - **Frontend:** Next.js, TypeScript, Tailwind - **Mobile:** Android, Kotlin *Location: In-person in Kendall Square, Cambridge (next to MIT and the Charles River).*
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