Not every feature is ready for everyone at once, and figuring out who sees a new experience first, and when to flip it on for everyone else, turns out to be trickier than it sounds once real users are attached to the product. That is the puzzle this role sits in the middle of.
You would be joining as the person who keeps feature flag rollouts thoughtful rather than chaotic, working alongside product, design, and engineering rather than off to the side of them. This is a full-time, fully remote position with no office or country requirement attached to it, and the team is used to working with people scattered across quite different time zones. The category this role sits in, product operations, tends to attract people who like sitting at the intersection of data and human behavior rather than purely one or the other.
Most weeks involve planning and running research or analysis tied to how a feature flag is performing, whether that means talking to a handful of users in an early rollout group or digging into usage data once a flag opens up further. You take what you learn and turn it into something the team can act on, not just a slide of findings nobody revisits later. A good chunk of the job is collaboration: sitting with product and design to talk through what a phased rollout should look like, then following up with engineering on how the flag itself is behaving once it is actually live.
Say a new checkout flow gets flagged on for ten percent of users. Your job is to figure out, within a week or two, whether the drop in support tickets from that group is a real signal or just noise from a small sample. That kind of judgment call, made repeatedly across many small rollouts, is most of what fills the calendar here.
The pace varies a lot depending on how many flags are active at once. Some weeks center on a single rollout that needs close attention; others involve juggling smaller updates across several features simultaneously, which is where prioritization skills matter as much as the research itself. Nobody hands you a perfectly framed question either; a good chunk of the work is deciding what actually needs to be measured in the first place. That ambiguity is uncomfortable for some people and genuinely enjoyable for others, and it is worth being honest with yourself about which camp you fall into before applying.
A bachelor's degree in human-computer interaction, design, business, or a related field is expected, paired with real hands-on experience specifically around feature flag release work rather than adjacent experience alone. Two years, 24 months if you want the precise number, is the baseline the team is hiring against. Strong analytical instincts matter, since a lot of this job is reading data that is noisy or incomplete and still needing to say something useful about it. So does comfort speaking up in front of stakeholders who may not agree with your read of the data at first.
None of this needs to be at an expert level walking in, though the strongest candidates usually have at least one flag rollout they can point to that did not go as planned, and can talk through what they actually learned from it.
This role suits someone who is genuinely comfortable being wrong in public, since half of feature flag work is forming a hypothesis, testing it, and then having to say out loud that it did not hold up. People who came up through user research or product analytics roles usually adjust fastest, because the muscle of turning ambiguous signals into a clear recommendation is already built. What matters less than a specific job title before this one is a track record of following through from a hunch to an actual decision.
This role is budgeted at $111,500 a year, full-time. Remoteroles has the pay set at that level with a benefits package attached: health coverage, paid time off, and retirement plan matching, plus a stipend meant specifically for your remote setup or home office, since a decent chair and a second monitor are not optional extras when this is where you work every day.
Since the team spans time zones, planning meetings and stakeholder reviews get scheduled around a shared overlap window rather than a single fixed office day, and most of the research and analysis work fits comfortably outside that window. Figma files, shared dashboards, and a running decision log are how the team stays aligned between live conversations, so nobody has to reconstruct context from memory during a sync. Async updates in writing are expected to be clear enough that a teammate three time zones away can act on them without needing a follow-up call.
Send your application along with an example of a feature flag rollout or similar research project you have worked on, including what the data told you and what you recommended as a result. A first conversation usually covers your research approach in more depth, and a second focuses on how you would work with product and engineering day to day. Most candidates hear back on next steps within ten business days of applying. Bring questions about how flags are currently reviewed and retired, since that process tends to reveal a lot about how much autonomy this specific team actually gives the role.