Machine Learning Engineer – Internship

This is a fully remote position, open to applicants in California, +1 more state.

📋 Description

• Develop and sustain data pipelines that create training datasets for machine learning models and experimentation.

• Contribute to the infrastructure that facilitates distributed training workflows utilizing tools like PyTorch and Ray.

• Collaborate with workflow orchestration tools such as Airflow and Flyte to support multi-stage machine learning pipelines.

• Enhance reproducibility and reliability through dataset validation, monitoring, and testing processes.

• Work alongside machine learning engineers to assist in experimentation and model iteration.

• Optimize the performance and efficiency of data processing and training systems.

• Help evolve the architecture of the offline machine learning platform as it scales.

⛳️ Requirements

• A bachelor’s degree in Computer Science, Machine Learning, Systems, or a related discipline.

• A strong foundation in machine learning systems, distributed systems, or large-scale data processing gained through research or projects.

• Proficiency in Python and experience with data-intensive workloads.

• Familiarity with machine learning frameworks like PyTorch and TensorFlow and/or distributed systems such as Ray and Spark.

• Academic or practical experience with data pipelines, model training workflows, or large datasets.

• Excellent problem-solving abilities and a talent for translating research concepts into functional systems.

• A keen interest in developing scalable and reliable machine learning infrastructure.

• Proficient in English for professional verbal and written communications.

• Preferred: experience with workflow orchestration systems like Airflow or Flyte.

• Preferred: exposure to large-scale data platforms, including data lakes, warehouses, and streaming systems.

• Preferred: publications or research in machine learning systems, distributed systems, or related fields.

🏝️ Benefits

• Equity awards.

• Participation in company incentive programs, such as annual discretionary bonuses or sales commissions.

• Comprehensive health, life, and disability insurance.

• Commute subsidy.

• Employee stock ownership.

• Competitive retirement and pension plans.

• Generous vacation and personal days.

• New-parent leave and family-care programs.

• Office snacks.

• Mental health and wellbeing programs and support.

• Employee Resource Groups.

• Global Employee Assistance Program.

• Training and development initiatives.

• Volunteering and donation matching program.