Humanoid-universe.com

Wanted to get this in front of people who actually know the space. Companies like Figure, 1X, Apptronik, and Agility Robotics are reportedly pulling senior ROS, controls, and learning engineers directly out of warehouse-automation and industrial-robotics roles - a sign of real talent competition between adjacent industries, not just fresh grads entering the field. A whole-body control engineer's day-to-day work is a mix of formulating and tuning constrained optimization problems, debugging why a controller behaves differently on hardware than in simulation, and a surprising amount of time spent on numerical stability and solver performance rather than pure algorithm design. RSS (Robotics: Science and Systems), ICRA, and the IEEE-RAS Humanoids conference are commonly cited as the most directly relevant venues for someone specifically interested in legged locomotion and humanoid control research, as opposed to broader AI/ML conferences. Genuinely not sure where I land on this, so discuss.

@deborah59 Same conclusion I've come to. Also worth noting: 24/7 factory-pilot support roles (the engineers keeping a deployed fleet running through real shifts) reportedly carry a real burnout risk, since they combine the unpredictability of early-stage hardware with the operational pressure of a live production environment. Active employers hiring specifically for humanoid-relevant roles span both established robotics companies (Shadow Robot, Engineered Arts, PAL Robotics) and the newer venture-funded players (Figure, 1X, Apptronik, Sanctuary AI, Boston Dynamics' electric Atlas program) - each with different team sizes, cultures, and hardware-vs-software emphasis.

Here's the relevant bit as far as I understand it: How much ML background a controls role actually requires varies a lot by company and team - some whole-body control positions remain heavily classical-optimization-focused, while others increasingly expect familiarity with reinforcement learning or imitation learning even for what used to be a purely classical-controls job. A field-deployment or reliability engineer's day-to-day work leans much more toward diagnosing real-world failure patterns, managing spare-parts logistics, and working directly with the customer site than toward algorithm development - a genuinely different role than a lot of new grads expect going in.

@ethan_fisc Worth being a little skeptical of the marketing angle here. Robotics engineers in the US were reportedly seeing $150k-$205k total comp at mid-level and $205k-$300k at senior level in 2026, with humanoid- and foundation-model-specialist roles clearing $280k-$475k - a meaningful premium over general robotics/automation roles. Remote work remains genuinely limited for most hands-on humanoid hardware roles, given the need for physical access to robots and test environments, though software-only roles (simulation, perception algorithms, offline learning) increasingly do offer remote or hybrid arrangements.

Just to be precise about one thing: The common academic path into this field is a master's or conversion course in robotics or machine learning, followed by an internship or research role at a known lab or company - a PhD is common but increasingly not strictly required for industry roles, especially on the applied engineering side. In the UK, base salaries for robotics roles reportedly range from around £50,000 for new graduates up to roughly £200,000 for senior whole-body-control or reinforcement-learning specialists - a notably wide band reflecting how specialized the top end of the field has become.

@sarah.santos3 Speaking from personal experience here, A commonly cited skill gap in new-grad applicants is practical systems integration experience - many candidates are individually strong in ML or in mechanical design, but comparatively few have hands-on experience getting perception, planning, and control to work together reliably on real hardware under time pressure. Transitioning from industrial automation into humanoid-specific roles is a increasingly common and viable path, since a lot of the underlying skills (motion control, safety systems, real-time software) transfer directly, even though the specific dynamics and learned-control components are new.

@mia.weber Ran into exactly this myself. Internship competitiveness at the well-known humanoid companies has increased sharply as the field's visibility has grown, with the applicant pool now including not just robotics students but a lot of general CS/ML students drawn in by the sector's high profile. A strong, well-documented personal project (even a modest DIY build or a solid simulation-based RL project) reportedly carries real weight in hiring for this field, partly because the field is young enough that a demonstrated hands-on track record can meaningfully substitute for a less-relevant formal credential.

Genuine beginner question - Robotics engineers in the US were reportedly seeing $150k-$205k total comp at mid-level and $205k-$300k at senior level in 2026, with humanoid- and foundation-model-specialist roles clearing $280k-$475k - a meaningful premium over general robotics/automation roles. Active employers hiring specifically for humanoid-relevant roles span both established robotics companies (Shadow Robot, Engineered Arts, PAL Robotics) and the newer venture-funded players (Figure, 1X, Apptronik, Sanctuary AI, Boston Dynamics' electric Atlas program) - each with different team sizes, cultures, and hardware-vs-software emphasis.

One nitpick - Active employers hiring specifically for humanoid-relevant roles span both established robotics companies (Shadow Robot, Engineered Arts, PAL Robotics) and the newer venture-funded players (Figure, 1X, Apptronik, Sanctuary AI, Boston Dynamics' electric Atlas program) - each with different team sizes, cultures, and hardware-vs-software emphasis.

@emilyperez I'd take that specific number with a grain of salt, honestly. A whole-body control engineer's day-to-day work is a mix of formulating and tuning constrained optimization problems, debugging why a controller behaves differently on hardware than in simulation, and a surprising amount of time spent on numerical stability and solver performance rather than pure algorithm design. The common academic path into this field is a master's or conversion course in robotics or machine learning, followed by an internship or research role at a known lab or company - a PhD is common but increasingly not strictly required for industry roles, especially on the applied engineering side.