Machine Learning Engineer (Egocentric 3D Human Pose) at Glint Tech Solutions LLC
Santa Clara, CA
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Aug 19, 2026
Santa Clara, CA
Aug 20, 2026
What this job asks for AI summary
This role focuses on building ML models and production pipelines that reconstruct accurate 3D human body and hand motion from egocentric (first-person) video, ultimately feeding robot learning systems. The engineer will work across the full perception stack — camera calibration, model training, large-scale inference, and data annotation — and collaborate with robotics engineers to convert human motion into robot training data. Strong computer vision and deep learning backgrounds are central to the work.
Mid level · 3+ years · Full-time
“or” means any one of them counts — you don't need all of them.
We read this from the posting text with AI. Skim the description below before ruling yourself out.
How this req sits in the market our data
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $122,874 (middle half $87,544–$162,374).
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Aug 20, 2026. It is a model, not a headcount.
Why we read it this way (7)
No work location is specified in the posting; CBSA and state are left blank. The role does not appear to be advertised as remote.
The SOC classification is a close call: the role is primarily ML research and model development (15-2051 Data Scientists) but involves substantial production engineering and pipeline work that could also fit 15-1252 Software Developers. Data Scientists was chosen because the core deliverable is novel ML models and perception research.
Degree requirement is set to None because the posting lists 'Bachelor's, Master's, or PhD' as options with no single minimum, and equivalent experience is implicitly accepted across that range.
SMPL, SMPL-X, and MANO are listed together in both the responsibilities and preferred qualifications sections; they are grouped as alternatives under a single skill since they serve the same body-model function. Their appearance in responsibilities is as context for the work, while the preferred section explicitly calls out expertise — marked preferred accordingly.
3D human pose estimation, camera calibration, and multi-view geometry are treated as hard gates based on their placement in the Minimum Qualifications section.
No compensation figures are provided beyond a general mention of 'competitive salary and equity.'
Ignored 1 non-technology phrase(s) as skills (responsibilities/concepts, not named tools): 3D human pose estimation.
Read the full posting
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