Engineering Manager, Machine Learning
RunwaySalary not specified
Full Time
Junior (1 to 2 years), Senior (5 to 8 years)
Not SpecifiedCompensation
Mid-level (3 to 4 years)Experience Level
Full TimeJob Type
UnknownVisa
Recycling, Waste ManagementIndustries
Requirements
- Previous experience with data annotation and quality control processes
- Comfortable spending 30-50% of time on customer deliverables and at customer facilities across recycling, cement, steel, and more
- Strong analytical skills with ability to identify patterns in model behavior
- Excellent communication skills - able to explain technical concepts to facility operators
- Self-starter mentality with ability to work independently in industrial environments
- Preferred
- Background in IT, computer science, or equivalent professional experience
- Experience with computer vision or ML model deployment
- Familiarity with Python and UNIX command line
- Prior customer support or field engineering experience
- Knowledge of recycling industry or waste management processes
Responsibilities
- Quality Control & Data Management
- Review and validate annotations completed by external annotation teams for recycling material classification
- Establish and maintain quality standards for annotation workflows specific to waste stream data
- Identify patterns in annotation errors and implement corrective processes
- Train customer teams on proper data collection and labeling practices for their facility's waste streams
- Customer-Facing Model Support
- Spend significant time at recycling facilities understanding their operational needs
- Monitor model performance in production environments and proactively identify issues
- Translate customer feedback into actionable data and model improvements
- Serve as the primary technical point of contact for model behavior questions
- Cross-Functional Collaboration
- Work closely with Visia's Machine Learning Engineering (MLE) team to communicate model performance issues discovered in the field
- Request and spec tooling upgrades based on observations from recycling facility floors
- Conduct root cause analysis of model errors in CCTV and X-ray detection systems
- Bridge communication between facility operators and engineering teams
Skills
Data Annotation
Annotation Quality Control
Computer Vision
Model Performance Monitoring
Customer Engagement
Root Cause Analysis
Machine Learning
Waste Classification
Quality Standards
Data Labeling
Visa
Global digital payment network provider
About Visa
Visa operates a global digital payment network that facilitates electronic payments for millions of people daily. The company connects consumers, businesses, financial institutions, and governments, allowing them to make transactions using Visa cards. Each time a card is used, Visa earns money through transaction, service, and data processing fees. Unlike many competitors, Visa focuses on expanding access to financial services for underserved communities and supporting local economies. The company's goal is to promote financial inclusivity and drive sustainable commerce, ensuring that more people can participate in the global economy.
San Francisco, CaliforniaHeadquarters
1958Year Founded
$55.8MTotal Funding
ANGEL_INDIVIDUALCompany Stage
Fintech, Financial ServicesIndustries
10,001+Employees
Benefits
Health Insurance.
Life Insurance.
Dental Insurance.
Disability Insurance.
Accidental Death & Dismemberment Insurance.
Risks
CBDCs could reduce reliance on Visa's payment network.
'Buy Now, Pay Later' services may decrease traditional credit card transactions.
Fintech startups offering zero-fee transactions could pressure Visa's revenue model.
Differentiation
Visa operates a global digital payment network connecting millions daily.
The company focuses on financial inclusivity and sustainability in its operations.
Visa collaborates with central banks on Central Bank Digital Currencies (CBDCs).
Upsides
Visa's partnership with fintechs enhances cross-border payment solutions.
Adoption of blockchain technology could revolutionize digital payments.
AI-driven fraud detection systems are reducing fraudulent activities.
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