Humanoid-universe.com

Pretty much this. One thing to add: 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. 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.

I can speak to this a bit. 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.

Small correction on one detail: 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. 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.

This matches what I've seen too. 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. 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.

@deborah59 I dealt with almost this exact situation. 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. 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. This whole thread is a good reminder how young this field still is.

@gimbalyuk55 This matches what I've seen too. 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.

@jessica_faro Can I ask a dumb follow-up - 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.

@lukas.singh1 I'd frame this differently. 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.

@erik_novi Here's the relevant bit as far as I understand it: 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.

@arjunsanchez +1 to this. Worth adding: 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. 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.