Software Engineer V – Applied ML Research (Robotics)

Location: Charlestown, MA, United States; hybrid work from the headquarters at least 3 days a week

Company

hirify.global develops physical AI and warehouse robots that automate truck and container unloading across global supply chains.

What you will do

• Own the machine learning intelligence for robotic grasping, including gripper configurations and tool selection.
• Translate machine learning research into production systems by modifying architectures, managing training pipelines, and deploying models to the robotic fleet.
• Design evaluation infrastructure using real-world field telemetry and live trailer environments.
• Develop and validate grasping and manipulation strategies in high-fidelity robotics simulations before hardware deployment.
• Collaborate with perception and motion-planning engineers through design discussions, code reviews, and debugging.
• Use AI coding assistants and modern development workflows to accelerate engineering delivery.

Requirements

• 6+ years of experience shipping and maintaining end-to-end machine learning systems; an MS degree counts as two years of experience.
• Experience deploying machine learning models in complex physical robotic systems.
• Deep expertise in custom neural-network and model design, beyond off-the-shelf APIs.
• Experience architecting and running medium- to large-scale cloud training pipelines.
• Mastery of Python, modern software engineering practices, and classical robotics concepts including motion planning, kinematics, geometry, and perception.
• BS or higher in Robotics, Computer Science, or a related technical field, with the ability to work from the Charlestown headquarters at least 3 days per week.

Nice to have

• Experience spanning perception and motion planning.
• Robotics simulation experience with Isaac Sim or MuJoCo.
• Experience with GCP.
• Daily use of AI coding assistants.

Culture & Benefits

• Work on physically deployed robots operating in warehouse logistics environments.
• Collaborate in a small applied ML team of two.
• Combine classical robotics with state-of-the-art neural networks.
• Work in an engineering environment focused on real-world operational performance rather than static benchmarks.