포지션 상세
Who We Are
Deeping Source develops software to derive meaningful actions from observable data using pre-installed CCTV setups in real-world environments with Vision AI models. This approach is commonly referred to as MTMC (Multi-Target Multi-Camera).
Our product aims to deliver actual actions that the consumer can take to enhance their retail margin or increase foot traffic while reducing overall maintenance cost.
Life at Core Team
• You will be assigned targeted goals that demand real problem-solving
• Juniors get a starting point on how to approach the problem
• Seniors are expected to design their own approach end-to-end
• The team respects each contributor's call on how to tackle the work
• Everyone is an individual contributor, including management
• Build the product layer on top of MTMC data that end users actually consume
• Design for scale as retail deployments grow and data volumes increase
• Productize demos that match prospective customers' potential needs
• Turn demos into consumer-grade software non-technical users can operate
Disclaimer: While this role relates to the use of vision AI or LLM, it is not a research role that is oriented towards training and validating models
• Bachelor's degree in Computer Science or a related field (Mathematics,Statistics, Electrical Engineering, etc.)
• Work-level English with strong verbal communication
• Strong backend or frontend coding skills. Regardless of which language you use, we're looking for people who can write clean and effective code and understand how data pipelines work.
• Working understanding of Machine Learning, specifically Vision AI and LLMs
• Strong problem-solving and critical thinking, with attention to detail
Preferred Qualifications
• 3+ years of experience in Backend, Machine Learning, or Frontend roles
• Experience with prompt engineering
• Experience with system design
• Experience with systems languages (Rust, C++)
• The development and validation phase has been extremely optimized since the rise of LLMs and we strongly differentiate between vibe coding and agentic coding
Deeping Source develops software to derive meaningful actions from observable data using pre-installed CCTV setups in real-world environments with Vision AI models. This approach is commonly referred to as MTMC (Multi-Target Multi-Camera).
Our product aims to deliver actual actions that the consumer can take to enhance their retail margin or increase foot traffic while reducing overall maintenance cost.
Life at Core Team
• You will be assigned targeted goals that demand real problem-solving
• Juniors get a starting point on how to approach the problem
• Seniors are expected to design their own approach end-to-end
• The team respects each contributor's call on how to tackle the work
• Everyone is an individual contributor, including management
주요업무
Core Responsibilities• Build the product layer on top of MTMC data that end users actually consume
• Design for scale as retail deployments grow and data volumes increase
• Productize demos that match prospective customers' potential needs
• Turn demos into consumer-grade software non-technical users can operate
Disclaimer: While this role relates to the use of vision AI or LLM, it is not a research role that is oriented towards training and validating models
자격요건
Minimum Qualifications• Bachelor's degree in Computer Science or a related field (Mathematics,Statistics, Electrical Engineering, etc.)
• Work-level English with strong verbal communication
• Strong backend or frontend coding skills. Regardless of which language you use, we're looking for people who can write clean and effective code and understand how data pipelines work.
• Working understanding of Machine Learning, specifically Vision AI and LLMs
• Strong problem-solving and critical thinking, with attention to detail
Preferred Qualifications
• 3+ years of experience in Backend, Machine Learning, or Frontend roles
• Experience with prompt engineering
• Experience with system design
• Experience with systems languages (Rust, C++)
• The development and validation phase has been extremely optimized since the rise of LLMs and we strongly differentiate between vibe coding and agentic coding
