Why This Job is Featured on The SaaS Jobs
This Senior Machine Learning Engineer role stands out in SaaS because it sits where product data, cloud infrastructure, and model capability converge. Inspiren’s offering implies a workflow-centric platform that must translate real-world activity into usable signals, which makes the edge to cloud boundary a core part of the product, not a side concern. Work on vision language models in this context is less about isolated model training and more about building a dependable intelligence layer that can operate within operational constraints.
For a SaaS career, the role offers durable experience in production ML systems that behave like product features. Ownership of labeling pipelines, evaluation frameworks, and inference integration maps directly to how modern SaaS teams ship and maintain AI functionality over time. The cross-functional loop with embedded, computer vision, and data science also mirrors the way SaaS organizations increasingly blend platform engineering with applied ML to improve accuracy, reliability, and iteration speed.
This position is best suited to engineers who prefer end-to-end accountability and measurable system outcomes over research exploration alone. It fits someone comfortable making tradeoffs among latency, bandwidth, and model quality, and who enjoys collaborating across disciplines to turn ambiguous real-world inputs into consistent product behavior.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
About Inspiren
Inspiren offers the most complete and connected ecosystem in senior living. Founded by Michael Wang, a former Green Beret turned cardiothoracic nurse, Inspiren proves that compassionate care and technology can coexist - bringing peace of mind to residents, families, and staff.
Our integrated solutions seamlessly fit into existing workflows, capturing everything happening within a community. Backed by nurse specialists and powerful analytics, we provide the data operators need to make informed clinical and operational decisions - driving efficiency, profitability, and better care outcomes.
About the Role
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.
What you'll own
- 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
What you bring
- 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
Bonus skills
- 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
Details
- The annual salary for this role is $200,000-$230,000 + equity + benefits (including medical, dental, and vision)
- Flexible PTO
- Location: Remote, US or Canada. NYC preferred.
- Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender perception or identity, national origin, age, marital status, protected veteran status, or disability status.
