What's the most useful single course you took for this career path?

[diego.moore6]

Ran into this exact question at work this week and wanted a sanity check. 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. 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. Interested to see if this matches what others are seeing.

[mohammed.rossi]

This is exactly the kind of context I was looking for. 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.

[ssantos]

@mohammed.rossi Tangent, but worth mentioning: 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.

[diego.moore6]

Ran into exactly this myself. 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.

[ssantos]

@diego.moore6 Genuinely curious - 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. This whole thread is a good reminder how young this field still is.

[deborah59]

Here's what I know on this: 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. 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. Reminds me a bit of the early drone hobbyist scene, honestly.

[nancy_lewi]

@deborah59 This lines up with my experience. 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.

[sarahbernard]

@nancy_lewi 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. 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. Totally unrelated but has anyone else noticed how fast component costs are dropping this year.

[jhansen]

@sarahbernard 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.

[byang]

@jhansen Not sure I fully agree here. 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.