Senior Machine Learning Engineer

Inspiren•New York, NY

1d•Remote

About The Position

We're looking for a Senior ML Engineer to build intelligent, scalable workflows powered by cloud-based Vision-Language Models (VLMs). You'll own the end-to-end pipeline: from determining what signals and context need to come off the device to maximize VLM performance, to building and evaluating the cloud-side inference systems that generate high-quality labels at scale. This role sits at the intersection of computer vision, LLMs, and systems engineering, and you'll work closely with our Embedded Systems, Computer Vision, and Data Science teams to close the loop between edge devices and cloud intelligence.

Requirements

5+ years of experience in machine learning engineering, with hands-on work in computer vision and/or LLM/VLM systems
Strong familiarity with Vision-Language Models — you've used, evaluated, or fine-tuned models like GPT-4V, Claude's vision capabilities, Gemini, LLaVA, or similar
Experience building and evaluating labeling systems at scale
Solid understanding of how edge/device constraints (bandwidth, compute, power) shape what data is available to cloud-side models
Proficiency with a modern ML stack: Python, PyTorch, cloud inference APIs, and tools for experiment tracking and evaluation
Practical prompt engineering skills — you know how to get the most out of large models through structured prompting, few-shot examples, and iterative refinement
A pragmatic engineering mindset — you care about building systems that work reliably in production, not just in notebooks
Comfortable scoping and driving work independently in a fast-moving, early-stage environment
Strong communication skills and a collaborative approach to working across hardware, embedded, and ML teams

Nice To Haves

Experience building model-based automation for data labeling
Experience building infrastructure for cloud-based inference at scale, for example batching, orchestration, cost management, and monitoring
Familiarity with edge computing, on-device inference, or embedded ML pipelines
Experience with video or spatiotemporal data (event streams, multi-frame reasoning, temporal context)
Healthcare or senior living domain experience

Responsibilities

Design, build, and iterate on VLM-based pipelines that generate high-quality labels and annotations at scale, including prompt engineering, fine-tuning, and evaluation
Determine which signals, frames, metadata, and contextual features should be sent from edge devices to improve VLM accuracy and reduce ambiguity
Collaborate with Embedded Systems and Hardware teams to define device-side preprocessing and data-forwarding strategies that balance bandwidth, latency, and model performance
Collaborate with Data Science to build robust evaluation frameworks to measure label quality, model accuracy, and regression detection
Benchmark and integrate commercial and open-source VLMs, staying current on the fast-moving landscape of vision-language capabilities