ML Engineer: Recommender Systems for Large-Scale Impact

Pantera Capital; Palo Alto (CA); On-site; USD 180,000 - 440,000; Full time

Benefits offered by this job

Equity

Medical coverage

Vision coverage

Dental coverage

401(k) retirement plan

Disability insurance

Life insurance

Employee discounts

Job summary

SpaceXAI is seeking exceptional Applied engineers to join a high-priority project used by hundreds of millions of users monthly. You will work at the intersection of advanced AI development and real-world impact, applying your skills to recommendation systems, ranking algorithms, search technologies, and more.

You will design and implement algorithms, utilize SpaceXAI infrastructure, and build data pipelines and training jobs that learn from product data.

Qualifications

• Knowledge of data infrastructure like Kafka, Clickhouse, and Spark.
• Experience building industrial-scale recommender systems or deep learning applications.
• Proficiency with DL frameworks such as JAX or PyTorch.
• CUDA kernel experience is a plus.

Responsibilities

• Designing and architecting recommendation algorithms across various product surfaces
• Leverage SpaceXAI's infra and AI stacks to dramatically enhance the user experience
• Write data pipelines and training jobs that continuously learn from product data.
• Iterate and improve the algorithm by gathering user feedback in real time through experimentation
• Ensuring scalability and efficiency of machine learning systems

Skills

Kafka

Clickhouse

Spark

Recommender systems

DL frameworks (JAX, PyTorch)

CUDA kernels

Job description

SpaceXAI is seeking exceptional Applied engineers to join a high-priority project used by hundreds of millions of users monthly. You will work at the intersection of advanced AI development and real-world impact, applying your skills to recommendation systems, ranking algorithms, search technologies, and more.

You will design and implement algorithms, utilize SpaceXAI infrastructure, and build data pipelines and training jobs that learn from product data.