Responsibilities
- Partner with applied scientists to create, implement, and monitor deep learning and machine learning systems for indoor positioning and object tracking, managing challenges from concept through deployment.
- Construct and refine pipelines for training, inference, and evaluation to transition models from experimental stages into scalable production environments.
- Tune models and infrastructure to balance precision, coverage, response time, and operational cost, making deliberate trade-offs across these dimensions.
- Create datasets and tooling to evaluate system performance under real-world, large-scale conditions.
- Work alongside engineering and product teams to identify high-value initiatives and deliver features to enterprise clients.
- Elevate team-wide engineering standards by contributing reusable systems, improved architectural practices, and sound technical assessments.
Work Arrangement
On-site — Kendall Square, Cambridge
Why Now
The company is an agile MIT spinout at a pivotal moment in its product evolution, assembling a high-caliber team to tackle complex challenges in retail operations. Engineers will have significant autonomy, direct impact, and rapid personal growth, with strong expectations for technical excellence and leadership.
