Senior Machine Learning Engineer at Bumble developing scalable AI systems for personalized user interactions. Leading machine learning model development and deployment from exploration to production.
About the role
- Senior AI/ML Engineer developing and deploying machine learning models for ADAS technology. Leading technical efforts and collaborating with diverse teams to enhance map content.
Responsibilities
- Our team is looking for a Senior AI/ML Engineer to be responsible for developing and deploying machine learning and statistical models and algorithms to support creating or enhancing map content for our ADAS/hands-free driving technology.
- It is expected that the person exerts technical leadership and guidance for the team in their area of expertise.
- This will include leveraging state-of-the practice tools and techniques to produce innovative solutions to complex problems.
- Design, build, and deploy predictive models and machine-learning algorithms to extract features, identify patterns, and classify outcomes using state-of-the-art frameworks and methods where practical.
- Create and enhance map data and related pipelines using cloud infrastructure and high-performance compute platforms.
- Analyze large datasets to discover trends and patterns.
- Present complex information using data visualization techniques.
- Conduct design, algorithm, notebook, and code reviews focused on machine learning and statistical analysis fundamentals, Python and relevant library best practices, and quality control and performance optimization principles.
- Document solution designs and development processes clearly and thoroughly.
- Collaborate with other ML Engineers, Data Scientists/Analysts, Software Engineers, and Product team representatives to design E2E solutions and strategies involving machine learning frameworks and big data workflows.
- Provide technical guidance and subject matter expertise to team members and stakeholders.
Requirements
- Master’s Degree in Engineering, Computer Science, Physics, Mathematics, or related quantitative field
- 6 or more years proficiency in one or more core analytical tools / suites / languages such as Python, PySpark, Spark, PyTorch, TensorFlow, and understand their limitations
- Minimum of 4 years of engineering/technical experience outside of a graduate school assistantship
- Demonstrable proficiency in statistical modeling and machine learning frameworks and tools
- Strong foundation in developing algorithms, data structures, and design patterns.
- Experience working with cloud infrastructure, high-performance compute systems, and parallel compute and data storage architectures.
- Experience leading projects and/or teams and managing customer requirements around scope, schedules, and deliverables.
Benefits
- From day one, we're looking out for your well-being–at work and at home–so you can focus on realizing your ambitions.
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
Lead Machine Learning Engineer at Bumble shaping user connections through machine learning. Driving end - to - end AI solutions while mentoring engineers in a hybrid work environment.
Designing and operating cloud - based MLOps capabilities supporting analytical and generative AI models. Collaborating with data science and business teams for high - impact AI solutions.
Machine Learning Engineer analyzing data structures and developing ML models for customer profiling in Azerbaijan. Collaborating on probabilistic modeling and data quality improvement.
Machine Learning Engineer at HackerRank working on integrity systems to improve model quality. Collaborating on strategies for new signals like audio analysis and behavioral anomalies.
Machine Learning Engineer developing integrity systems for assessing model quality at HackerRank. Collaborating on multimodal signal processing and improving model performance.
Architect designing enterprise - grade AI/ML architectures for Quantiphi. Leading AI applications and ML strategy with a focus on scalability, security, and integration.
Software Engineer for ML Infrastructure at Slack, architecting systems to support large scale AI deployment and reliability. Engage in deep systems engineering focusing on ML lifecycle and infrastructure scalability.
Machine Learning Engineer at Winnow developing AI solutions for food waste reduction. Collaborate with cross - functional teams and leverage cutting - edge technologies in food recognition.
Senior Engineer developing AI/ML solutions to enhance patient care at Edwards Lifesciences. Collaborating with cross - functional teams to deliver impactful technologies in healthcare.