Senior Machine Learning Engineer - Perception

Location: Charlestown, MA, United States; Workplace: Hybrid

Company

hirify.global develops physical AI and warehouse robots that automate unloading and loading tasks across global supply chains.

What you will do

• Frame ambiguous robot picking goals as well-defined machine learning problems, including predictions, labels, rewards, losses, and success criteria.
• Own dataset strategy through synthetic scene generation, distributed cloud jobs, diversity filtering, self-supervised labeling, and active learning.
• Train and iterate on large vision backbones with task-specific heads and disciplined experimentation.
• Define offline and online evaluation metrics that predict on-robot outcomes and improve edge-case performance.
• Review code, mentor earlier-career engineers, and contribute to the perception and grasping roadmap.
• Prepare deployable models with latency budgets, exportable graphs, and reproducible artifacts for runtime integration partners.

Requirements

• 5+ years of experience in machine learning and computer vision, including ownership of models from problem statement to measured outcome.
• Experience translating product requirements into engineering tasks and explaining technical trade-offs to non-expert stakeholders.
• Hands-on experience building datasets at scale, including curation, automated quality assurance, coverage and bias analysis, and active learning.
• Strong evaluation practice with baselines, ablations, metric design, and result verification.
• Expert-level Python and PyTorch skills, plus experience with GCP or AWS, Docker, and experiment tracking tools.
• Professional experience developing and training multi-task, multi-modal, multi-view, self-supervised, outcome-based, or reward-driven models.

Culture & Benefits

• Hybrid work arrangement in Charlestown, Massachusetts.
• Work on physical AI for warehouse automation and logistics.
• Close collaboration with platform, robotics, runtime integration, and inference optimization engineers.