DataOps Engineer

Sanctuary in Vancouver, BC

Your New Role and Team
Sanctuary - a multi award-winning LinkedIn Top Startup company - is looking to hire a DataOps Engineer for our Data Collection team. Reporting to the Data Collection Team Lead, you’ll gain a comprehensive understanding of the data collection tools and ML training pipelines that power our general Purpose Robots (GPRs).
The best candidate for this role will be a versatile, creative engineer with proven experience in supporting data lake structures, setting up DataOps processes, writing data annotation scripts, data exploration, and feeding data into large-scale ML pipelines and frameworks. You’ll be a valued contributor as you learn how our sophisticated multi-degree-of-freedom robotic systems work, while collaborating with multi-functional teams, including ML, Platform, Product Design, and Hardware and Sensor teams, to collect, validate and process large data sets.
Working at Sanctuary AI
Sanctuary AI is an equal opportunity employer; employment with Sanctuary AI is governed based on skills, competence, and qualifications and will not be influenced in any way by race, color, religion, gender, national origin/ethnicity, veteran status, disability status, age, sexual orientation, gender identity, marital status, mental or physical disability, or any other legally protected status. In 2023, Sanctuary AI moved into a state-of-the-art office facility and has been recognized by LinkedIn as a Top Startup company.
Compensation and Benefits
Sanctuary offers a market-leading compensation package that includes competitive salaries, equity stakes, and a full suite of benefits for permanent employees, encompassing health coverage, paid time off, cutting-edge work facilities, and worksite flexibility by role. Our commitment to fairness ensures that our total compensation consistently surpasses market standards.

About Sanctuary AI
Founded in 2018, Sanctuary builds humanoid robots and a novel control system for them that integrates symbolic logic and reasoning with data-driven robot foundation models. We use our robots to collect vision, audio, touch, and proprioception data from the perspective of the robot while they perform real-world work tasks. We use that data to train multimodal robot foundation models. Because our systems are vertically integrated, we can design, deploy, and refine at scale. Our mission is to create the world's first human-like intelligence in general-purpose robots.




Recruiting and Employment Agency Notice:
Recruitment and hiring is conducted internally by Sanctuary AI. We are not seeking or soliciting any new agency partnerships or agreements at this time . Any employment agency or professional recruiter (“Agency”) that provides an unsolicited resume(s) or otherwise presents a prospective job candidate through the Sanctuary AI career site or directly to any Sanctuary AI employee, irrevocably grants to Sanctuary AI the unrestricted right to engage, hire, or contract with that candidate at Sanctuary AI's sole discretion without any compensation to the Agency. We appreciate your interest in working together, and should the need arise our Talent Acquisition team will contact any external firms directly.

    • Participate in data collection sessions to ensure their smooth execution
    • Acquire and annotate datasets that align with ML needs
    • Develop algorithms to transform robot telemetry into ML training and validation datasets
    • Build and support a secure, extensive, scalable, repeatable, and high-performing data collection platform
    • Systematically augment datasets in order to increase the accuracy of ML applications
    • Build and deploy ML model evaluation pipelines
    • Analyze collected data and show how the data contribute to the model performance
    • Build and implement tools that will support data analysis

      • Your Experience
      Qualifications:
    • Bachelor's degree or higher in Computer Science or related fields
    • 3+ years experience in a Data Platform Engineer, DataOps, or equivalent role
    • 2+ years of Experience with ML frameworks, platforms and tools
    • Basic understanding of machine learning algorithms, including reinforcement learning, behavior cloning, and generative AI
    • Knowledge of professional engineering practices for the full product life cycle, including coding standards, code reviews, source management, agile, processes, testing, and operations
    • Demonstrated ability to design, implement, and test in a fast-paced environment

      • Skills:
    • Ability to develop data collection efforts that focus on the end-to-end user experience, including anticipating potential failure modes, edge cases, and anomalies
    • Demonstrated proficiency with Python for data pipeline development
    • Demonstrated experience developing, optimizing, and maintaining data warehouse solutions
    • Demonstrated experience with setup and configuration of ML Ops platforms such as Weights & Biases and ClearML
    • Demonstrated experience with Observability platforms such as Splunk, Datadog, ELK Stack, and Prometheus/Grafana
    • Demonstrated familiarity with MLOps, ML, and deep learning techniques; including but not limited to Computer Vision, SLAM, NLP/NLU, and Robotics

      • Traits:
    • Above all else, a consistently positive attitude and a willingness to do whatever it takes to create robust solutions to complex problems (See pg. 3 for more trait options)[1]
    • Optimistic listening and conflict resolution capabilities
    • Strong verbal and written communication and interpersonal skills
    • Self-motivated and able to solve problems independently
    • Demonstrated ability to communicate to get things done
    • Obsession with bringing human-like intelligence to machines



Apply