One team
We work alongside our design partners as one team, not as a vendor across the table. Collaboration is what carried our species this far, and it's how we work.














We’re scientists, engineers and operators who’ve built at the frontier of technology — and who understand how the physical world actually runs. From Google, Meta, Amazon, and the Fortune 500, we've spent two decades solving hard problems at a planetary scale.
Now we're doing it again, somewhere harder: the real world. If you'd rather build frontier AI and watch it hold up outside the lab, we should talk.

We work alongside our design partners as one team, not as a vendor across the table. Collaboration is what carried our species this far, and it's how we work.
Self-improvement is the promise we build into our products and the standard we hold ourselves to: get a little better every cycle, in the lab and in the field, and let it compound.
Serious about the work, less so about ourselves. Every miss becomes a lesson, and every lesson makes the next attempt better.
We take our research straight into the messy, ambiguous real world and earn our way on the problems at the core of how a business runs. Proven in the field, not on a slide.

Co-Founder & CEO

Co-Founder & CPO

Chief Business Officer

Founding Member of Technical Staff

Founding Member of Technical Staff

Founding Member of Technical Staff

Founding Member of Technical Staff

Member of Technical Staff

Founding Member of Technical Staff

Founding Member of Technical Staff

Founding Member of Technical Staff

Founding Member of Technical Staff

Founding Member of Technical Staff

Founding Member of Technical Staff

Founding Member of Technical Staff

Founding Member of Technical Staff

Operations Manager
4 Job Openings
At Noetive, you'll push the frontier of what foundation models can do across language, vision, and world model development, designing and running experiments on post-training, reasoning, agentic behavior, and evaluation, then translating those findings into capabilities that ship in real products. You'll work with a small, principal-level team that moves fast from hypothesis to result, own the full arc from literature review through ablations to writeup, and hold a high bar for rigor even under startup timelines. We're looking for a PhD or equivalent research track record, deep fluency in modern deep learning (PyTorch, distributed training, LLM fine-tuning and RL), a strong publication or open-source footprint, and the instinct to pick problems that matter rather than problems that are merely publishable.
As an AI Native Full Stack Engineer you'll build the products and infrastructure through which our research reaches users: agentic workflows, LLM-backed services, evaluation harnesses, and the interfaces that make them usable. You treat language, vision, and world models as first-class components of the system, understand their failure modes, and design for latency, cost, and non-determinism as everyday engineering constraints rather than afterthoughts. You'll own features end to end across front ends, back ends, and cloud infrastructure, and you'll ship hourly with AI coding tools as a natural part of your workflow. We're looking for strong product engineering fundamentals, hands-on experience integrating LLM APIs, embeddings, and tool use into production systems, and a bias toward getting working software in front of people.
As an AI Native Product Manager you'll define what we build and why, turning research capabilities in language, vision, and world model development into products that solve real problems for real users. You'll live at the intersection of researchers, engineers, and customers, writing crisp specs, running discovery, designing evals that double as product requirements, and making hard tradeoffs. You understand how these models actually behave, prototype with them yourself, and can tell the difference between a demo and a durable product. We're looking for several years of product experience shipping software people use, direct hands-on work with generative AI products, sharp written and analytical skills, and comfort leading in a fast, ambiguous environment where the roadmap is shaped as much by what the models can newly do as by what customers ask for.
Shape the systems we deploy and the engineering behind them
Noetive is an applied research lab building artificial intelligence that learns how industrial businesses operate. The company works with early design partners in manufacturing, logistics, and construction. We are developing sensing hardware to give that software reliable data about physical operations.
We’re looking for a hands-on technical leader to own deployment architecture and technical strategy for hardware systems installed and maintained in industrial settings. Your scope connects sensing devices, electronics, and firmware (the software that directly controls the hardware) with on-site computers, networks, power, and storage.
You’ll work with colleagues developing sensing technology and product strategy. You’ll identify the decisions that matter before there is a specification, challenge assumptions, and carry difficult engineering problems through to a proven solution. Your work should change what we deploy and how other engineers build it.
What you’ll lead
Deployment architecture. Own how sensing devices, on site computers, networks, storage, and power work together across industrial sites and future generations. Lead decisions about what to build or buy, with repeatable installation, maintenance, verified data backups, overnight transfers, secure remote diagnosis, and failure recovery built into the design.
Electrical and embedded engineering. Lead electrical and firmware design for deployed systems. Personally design and debug critical elements, bringing in specialists where deeper expertise is needed.
Deployment readiness. Set performance and reliability requirements and the evidence needed for release. Resolve risks that could invalidate the design, and use field results to shape future systems.
Technical leadership. Lead critical design decisions with colleagues and partners. Set engineering standards, strengthen experienced engineers’ work, and drive architecture changes across hardware and software.
What you bring
Architecture with lasting consequences. You have personally led major technical choices for industrial systems and followed them through installation and sustained use. You can show where you changed direction, why, and how the decision held up across sites or product generations.
Electrical engineering and embedded firmware depth. You can design and debug electronics, reason from power and signal measurements, and write and debug device firmware. Your experience includes device control and hardware interfaces, with drivers (software connecting hardware to an operating system) and devices running the Linux operating system where relevant.
Judgment grounded in deployment. You understand how networking, on-site computing, storage, backups, and maintenance constrain hardware design. You can trace a failure across those boundaries and decide where to investigate personally or bring in deeper expertise.
Independent technical leadership. You find important problems without waiting for assignments, make defensible decisions with incomplete information, and improve other experienced engineers’ results. You remain accountable for outcomes after the design review.
Founder or early startup experience is strongly preferred. Relevant backgrounds include robotics, industrial sensing, machine vision, integrated cameras, and other deployed industrial hardware. Scope and impact matter more than company size or title.