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Re: Anyone regret specializing too early before the field's direction was clearer?
Posted: Sun Aug 30, 2026 11:59 am
by betty.king
@mary.taylor6 Yeah, this tracks with what I've read as well.
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. 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.
Re: Anyone regret specializing too early before the field's direction was clearer?
Posted: Sun Aug 30, 2026 11:59 am
by mohammed64
@betty.king Not sure I fully agree here.
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.
Re: Anyone regret specializing too early before the field's direction was clearer?
Posted: Sun Aug 30, 2026 11:59 am
by diego.moore6
@mohammed64 Here's the relevant bit as far as I understand it:
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.
Re: Anyone regret specializing too early before the field's direction was clearer?
Posted: Sun Aug 30, 2026 11:59 am
by harmonicjen60
From hands-on experience,
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. 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.
Re: Anyone regret specializing too early before the field's direction was clearer?
Posted: Sun Aug 30, 2026 11:59 am
by mohammed.rossi
@harmonicjen60 This lines up with my experience.
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.
Re: Anyone regret specializing too early before the field's direction was clearer?
Posted: Sun Aug 30, 2026 11:59 am
by garcia51
@mohammed.rossi Slight correction, though the overall point stands:
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.