Senior ML Data & Ops Engineer

MaziCTools; United States; Remote; USD 176,000 - 312,000; Full time

Benefits offered by this job

Medical and dental coverage

Retirement benefits

Stock programs

Relocation assistance

Job summary

Apple is seeking an ML Ops Engineer for the Body-sensing Intelligence Group within AIML to deliver high-quality datasets for next-generation ML algorithms, translating ML-driven experiences into scalable data pipelines that consider hardware, firmware, and software needs.

The role involves building systems to monitor data quality, collaborating with researchers, engineers, designers, and data experts, and presenting solutions to diverse stakeholders.

Qualifications

• Holds a B.S./M.S. degree in Electrical Engineering, Bioengineering, Data Science, or a related discipline
• Possess over 5 years of experience in taking products from concept to large-scale production
• Skilled in rapid prototyping and familiar with Design of Experiments (DOEs)
• Excellent problem-solving abilities and a proactive mindset in addressing challenges
• Strong communication skills and experience collaborating with distributed cross-functional teams
• Strong organizational skills and a keen attention to detail

Responsibilities

• Partner with multidisciplinary teams to lead innovative data collection projects with a focus on privacy
• Oversee the creation of intricate user studies that support research aims and product objectives
• Innovate and implement new operational strategies and practices
• Evaluate and enhance existing metrics and protocols to boost data quality and efficiency
• Quickly adapt to new and emerging hardware and software technologies
• Play a key role in creating groundbreaking products that improve user experiences
• Manage multiple active projects involving both emerging and established technologies
• Thrive in a collaborative and dynamic setting with opportunities for growth.

Job description

Apple is seeking an ML Ops Engineer for the Body-sensing Intelligence Group within AIML to deliver high-quality datasets for next-generation ML algorithms, translating ML-driven experiences into scalable data pipelines that consider hardware, firmware, and software needs.

The role involves building systems to monitor data quality, collaborating with researchers, engineers, designers, and data experts, and presenting solutions to diverse stakeholders.