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Mentor - Machine Learning Engineering Career Track (Part-time/Remote)

Remote
The Company 
 
At Springboard, we’re on a mission to bridge the world’s skills gap, offering transformative online education in data science, UI/UX design, machine learning, and coding. Our courses may be tech-enabled, but we're ultimately human-centric: each student taps into a vast community throughout their time with us, engaging with fellow students, industry-expert mentors, student advisors, and career coaches, the goal of which is to successfully transition students into their dream job. Through this hybrid approach, we’ve helped thousands of learners revamp their careers and, by extension, their lives, with hundreds of top-notch job offers received every year and a near-perfect placement rate for our program graduates.

About the course

We’re looking for mentors for our Machine Learning Engineering Career Track course. This 6-month course is primarily designed for students who want to become Machine Learning Engineers.

As part of the course, students start with an introduction to basic ML algorithms and quickly advance to topics like large language models and generative AI. Through hands-on projects and practical exercises, they will master the entire machine learning pipeline, from data preprocessing and feature engineering to model deployment and scaling. They will gain proficiency in popular frameworks and tools like TensorFlow, Scikit-Learn, and AWS, equipping you with the ability to develop and deploy machine learning models at scale. The course goes beyond just the technical aspects of machine learning. Students also explore ethical considerations surrounding AI and learn how to build models that are fair, transparent, and unbiased. We'll cover topics like interpretability, bias detection, and privacy, ensuring students have a well-rounded understanding of the field. 

The Opportunity

Springboard runs an online Machine Learning Engineering Career Track Bootcamp in which participants learn with the help of a case-study-based curriculum and 1-1 guidance from an expert mentor.

We are currently focused on seeking Machine Learning Engineers with proficient skills in Artificial Intelligence Design, Python/Python Libraries,SQL, AWS, Model Evaluations and Model Deployment. These individuals must be passionate about mentoring as Machine Learning Engineering, and can give a few hours per week in return for an honorarium, we would love to hear from you.

Questions? Please write to us at mentorrecruiting@springboard.com

The Program:

  • Completely online and self-paced
  • Students become proficient in Machine Learning Engineering with the help of a curated online curriculum and project-based deliverables
  • Students are working professionals from all over the world
  • Students have a biweekly 30-minute check in with their mentor to discuss questions, projects, and career advice!
  • Students communicate with mentors outside of calls on an as-needed basis to support learning and career objectives

You:

  • Are as passionate about teaching coding as about machine learning itself
  • 3+ years of industry experience in machine learning engineering or data science
  • Experience in Python Data Science Stack; using python and its standard libraries; building visualizations with Matplotlib and Seaborn;Writing code in Python using PEP 8 Standard
    • Data Wrangling: Use Pandas to wrangle and clean data; working with different file formats, from plain text, to CSV, to JSON; relational and non-relational databases; SQL
    • Machine Learning: Scikit-learn; ML techniques: linear and logistic regression, trees, and clustering;
    • Deploying ML: Experience deploying ML solutions with a cloud service provider (at least one of AWS, GCP or Azure).
    • Deep Learning Fundamentals: Familiarity with deep learning, computer vision, natural language processing
    • Expertise in Python, libraries like TensorFlow and PyTorch, SQL, cloud ML, etc.
    • Experience working with large language models and generative AI, such as training and fine-tuning language models for specific tasks.
    • Knowledge of cloud-based deployment strategies for large AI models and practical experience with model versioning and reproducibility.
  • Degree/Certifications in machine learning/AI/data science preferred
  • Have experience critiquing work, in particular giving meaningful feedback on machine learning engineering and are able to think on your feet quickly
  • Are empathetic and have excellent communication skills
  • Bonus points if you know American Sign Language (ASL) or have teaching experience 

Benefits:

  • Receive a monthly per-student honorarium between $14.03 - $25.50 USD per 30 minute call per student 
  • Membership in a rich community of expert mentors from great companies like AirBnB, Uber, Google, and Pivotal
  • Change the lives of students in our program
  • Help us revolutionize online education and inspire the next generation of software engineers!
  • Work at your convenience

We are an equal opportunity employer and value diversity at our company. We welcome applications from all backgrounds, and do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

 
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Under the California Consumer Privacy Act (“CCPA”), Springboard is required to inform California residents who are job applicants about the categories of personal information we collect about you and the purposes for which we will use this information. This notice contains disclosures required by the CCPA and applies only to personal information that is subject to the CCPA.

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Please fill out this self skills assessment for us to better understand your knowledge of our curriculum. Please rate yourself on a scale of 1-5 (1=novice, 5=expert) for each of the following areas.

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Unit 1: Python Data Science Stack (Pandas, sci-kit learn, Keras, TensorFlow, spaCy); Data Engineering Tools (Spark/PySpark, Containers, Cloud Computing); Software Engineering (continuous integration, Github, testing, and debugging); Basic data structures and algorithms (lists, hash tables, trees, sorting, searching)

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Unit 2: Collecting data (APIs, real-time systems, web scraping); cleaning and transforming data for AI systems at scale; working with large data sets in SQL and NoSQL databases; tools such as Pandas, Spark, Dask, SQL, Spark SQL

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Unit 3: Supervised and unsupervised learning; linear and logistic regression, decision trees, and ensembles; scalable algorithms (XGBoost, Catboost); feature engineering and selection, model validation and interpretation; scaling ML models to large data sets; tools (scikit-learn, SparkML, AutoML systems)

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Unit 4: Overview of Neural Networks, backpropagation and foundational techniques; principles of Deep Neural Networks; common Deep Neural Network configurations (e.g. RNNs, CNNs, MLPs, LSTMs); Generative Deep Learning and GANs; engineering frameworks (Keras, TensorFlow, PyTorch)

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Unit 5: How to work with text and natural language data; NLP in Python using common libraries like NLTK and spaCy; representing language (BOW, TF-IDF, word embedding models [word2vec, GloVe, FastText]); Deep Learning techniques for NLP; chatbots and other modern NLP applications

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Unit 6: Foundations of computer vision and/or image processing; common challenges in vision (e.g. object recognition); Deep Learning for images and/or video

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