ML Engineer, II - New AI Initiatives

Remote - USfull timeRemote

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mid

$153k–$184k

H1B: yes

Torc Robotics ML Engineer II on the ML Ops team at Torc Robotics developing machine learning workflows and AI development tooling. Responsible for data science model development and project leadership.

Responsibilities

You will play a key role building the foundation of Torc's AI development tooling.
Independently develop data science models or algorithms using disciplined software development processes, making recommendations for developing new code or re-using existing code, enforcing version control, and maintaining documentation of created applications.
Operate with Data Ops to define and implement ingestion, data preparation, curation, and governance of large, multi-faceted data sets supporting analytics models and workflows.
Organize and lead projects, serving as project lead guiding less experienced team members in multiple facets of project execution
Empower users with key insights, supporting visualization and data accessibility in a customer centric manner.
Develop guidelines and standards for analytics and machine learning models, their deployment, and associated processes.
Communicate and collaborate within the team and participate with the team’s interfaces to the rest of the organization.

Qualification

Bachelor’s Degree in Computer ScienceMaster’s Degree in Computer ScienceRigorous dispositionYou will need resilience

Required

Bachelor’s Degree in Computer Science, Robotics, Electrical Engineering or related technical field plus demonstrates competences and technical proficiencies typically acquired through 4+ years of experience or;
Master’s Degree in Computer Science, Robotics, Electrical Engineering or related technical field plus demonstrates competences and technical proficiencies typically acquired through 0-3+ years of experience.
Adventurous spirit where you can work on topics at the frontier and drive exploration.
Rigorous disposition: you will have to pass judgement on what is and what is not practical for Torc to implement at scale.
You will need to communicate your outcomes and progress to other teams so they can take your findings and reliably implement them.
You will need resilience, you must be comfortable with working on a project for a month that may not reach production.