Why This Job is Featured on The SaaS Jobs
This role stands out in the SaaS ecosystem because it sits at the intersection of product, customer delivery, and applied machine learning for a cloud offering that is still early in its lifecycle. With an open source viewer already in use and a cloud product live with early design partners, the work reflects a common SaaS pattern: turning strong technical adoption into repeatable production deployments and learnings that shape what gets built next.
For a SaaS career, the long-term value is exposure to the full loop from pre sales technical credibility through onboarding and sustained usage. The emphasis on demos, pipelines, and production support mirrors how many modern SaaS companies win in technical categories, especially where customers need hands on guidance to reach time to value. Feeding customer lessons back into the roadmap also builds product intuition that transfers across developer tools and infrastructure SaaS.
This position is best suited to an engineer who enjoys context switching between deep technical work and direct customer interaction, and who is comfortable operating with incomplete answers while the product matures. It will appeal to someone motivated by ownership, practical problem solving, and periodic travel to unblock real deployments.
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
We’re building the data stack for Physical AI. The Rerun open source viewer is already loved by some of the best teams in the world and the Rerun Cloud is live with early design partners.
Demand is strong, and we need engineers who can sit with our customers and make them successful while feeding lessons learned back into the product. Our product is still early, and the right person will be comfortable fixing a feature and preparing a data pipeline as they are running a live demo.
What success looks like in the first 6 months
Your north star is simple: help our customers love Rerun. You'll be the primary technical interface between Rerun and a set of customers, from demo to signed contract and through deep production use. We value a strong ownership mentality.
What you'll work on
Machine learning:
Data pipelines: ingest data from physical systems, curate datasets, and convert into training datasets
Training and experimentation: building reference projects for embodied AI use cases such as imitation learning pipelines, sim-to-real setups, and evaluation frameworks
Customer love:
Landing customers: Running demos and building engineer-to-engineer trust alongside our founders based on machine learning work
Launching customers: Support onboarding customers onsite and remotely. We anticipate 30% travel for this role, with less initially and potentially growing to a larger percentage over time
Improving product: Mapping customer needs and one off solutions to evolve our roadmap. Sometimes building features yourself based on your deep customer understanding
We'd love it if you have
ML engineering background, ideally with data engineering exposure with a strong understanding of pipelines, ingestion, and the messy realities of physical-world data
Strong Python; C++ or Rust is a plus
Genuine interest in robotics, spatial AI, or computer vision
Comfortable talking to engineers and communicating value without losing technical depth
Energized by ambiguity, ownership, and wearing many hats
Comfortable with periodic travel and ideally excited by the prospect of being deeply embedded with a customer when it matters
How we work at Rerun
We're a remote company headquartered in Stockholm, Sweden.
We meet up in person for a week roughly once a quarter
The team you'll join has members in European and US timezones
We've put together an uncommonly talented tech team, value agency and helpfulness highly, and expect everyone to take broad responsibility for what they build
We offer competitive cash and equity compensation, six weeks paid vacation, and whatever hardware and software you need to do your job
