About Motorq
Motorq is the leading connected vehicle intelligence platform, processing over 5 million trip miles and 500 million data points per day. We aggregate data directly from automotive OEM APIs — no hardware required — and deliver it as structured insights and automated workflows to fleet operators, lenders, rental companies, and automotive partners.
Headquartered in the SF Bay Area with offices in Chennai, Bengaluru, and Seattle, Motorq has raised a $40M+ Series B and is growing rapidly across new geographies and use cases. We're part of the Stanford StartX community and deeply partnered with leading automotive OEMs including GM, Ford, Toyota, Stellantis, Volkswagen, Volvo, Tesla, Mercedes-Benz, Audi, and Rivian.
The Role
Motorq is building out a serious AI product layer — one that sits above our connected vehicle data foundation and turns raw fleet intelligence into autonomous workflows, predictive insights, and productivity-multiplying tools for the businesses that depend on it. We are looking for an experienced Product Consultant to help us build it right.
This is a part-time engagement — approximately 2 to 3 days per week — designed for a senior product thinker who wants meaningful, high-impact work without a full-time commitment. The role is structured with the explicit intention of growing into a full-time position as the engagement deepens and the AI product layer scales.
The Product Consultant will work across two connected dimensions. The first is internal: shaping Motorq's AI product strategy, roadmap, and build priorities — from LLM-powered insight engines and agentic workflows to evaluation frameworks and AI reliability standards for enterprise fleet environments. The second is external: going deep with Motorq's most important customers to understand their operational processes — how they manage maintenance, dispatch, driver performance, compliance, and end-of-life decisions — and identifying where AI can meaningfully increase productivity, efficiency, and the repeatability of those processes at scale.
The ideal candidate has shipped AI products in a production enterprise environment, knows how to run rigorous customer discovery, and can move fluidly between product strategy and hands-on definition. Experience in fleet, mobility, automotive, or industrial operations is a strong plus — but more important is a track record of turning complex operational contexts into well-designed, durable AI products.
What You'll Do
Contribute to the definition and prioritization of Motorq's AI product roadmap — working with Engineering, Data Science, and Product leadership to identify the highest-impact opportunities and sequence them intelligently.
Conduct deep operational discovery with Motorq customers — mapping their workflows, understanding their pain points and productivity bottlenecks, and identifying where AI-powered automation, prediction, or decision support can create the most value.
Translate customer process knowledge into crisp, actionable product requirements — defining what an AI solution needs to do, how success is measured, and what guardrails are needed for enterprise reliability.
Evaluate and recommend AI approaches for specific use cases — including LLM-powered interfaces, agentic task automation, RAG-based retrieval, predictive models, and multi-step workflow orchestration — matching the method to the problem.
Define AI product evaluation frameworks and reliability standards appropriate for high-stakes fleet operations — ensuring Motorq's AI products are trustworthy, explainable, and production-ready.
Partner with Engineering and Data Science to move from requirements to pilots — participating in design reviews, sprint planning, and pilot readouts to keep product and technical direction tightly aligned.
Identify patterns across customer processes and use cases that can be productized — turning one-off implementations into repeatable, scalable AI capabilities that Motorq can deliver across its customer base.
Stay at the forefront of the agentic AI and LLM ecosystem — bringing relevant new capabilities, frameworks, and approaches into Motorq's product thinking before competitors do.
Who You Are
A product thinker with deep AI fluency — you understand how AI systems are built well enough to make good product decisions, and you're energized by the challenge of applying them to real operational problems.
A process detective — you are naturally curious about how organizations actually work, you ask great discovery questions, and you can map a complex operational workflow quickly and accurately.
Customer-obsessed — you believe the best AI products start with a genuine understanding of the people and processes they serve, and you never skip that step.
Outcome-driven — you care about whether the AI you help build actually works in the field, not just in a demo, and you define success in terms of measurable customer impact.
Comfortable with ambiguity — you can make good product decisions with incomplete information, move fast in a startup environment, and refine as you learn.
A collaborative partner — you work well with engineering, data science, and customer-facing teams, and you bring others along without needing to own everything.
Qualifications
6+ years of product management experience, with at least 2 years delivering AI-powered or data-driven products in a production enterprise environment.
Hands-on experience with AI/ML product development — including LLMs, agentic workflows, RAG architectures, and evaluation frameworks.
Demonstrated ability to conduct structured customer discovery and translate operational process knowledge into well-scoped product requirements.
Experience identifying repeatable, scalable AI use cases from individual customer engagements — building toward a product, not just a custom implementation.
Background in fleet management, mobility, logistics, industrial operations, or connected vehicle platforms is a strong plus.
Technical fluency — able to work as a peer with engineers and data scientists, review technical approaches critically, and make informed build-vs-buy decisions.
Strong written communication — able to produce tight product specs, crisp roadmap narratives, and clear customer-facing documentation.
Logistics
Part-time engagement — approximately 2 to 3 days per week.
Remote.
Occasional travel to customer sites and the SF Bay Area for planning sessions and team meetings.
Structured as a contractor engagement, with a clear path to a full-time role.
Compensation
Competitive part-time compensation.
This engagement is structured with the clear intention to grow into a full-time role.
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