Senior Deep Learning NLP Engineer
Posted on 9/10/2023
INACTIVE
H1

201-500 employees

Locations
Remote
Experience Level
Entry
Junior
Mid
Senior
Expert
Desired Skills
Agile
AWS
Data Structures & Algorithms
Docker
Keras
Linux/Unix
Pandas
Pytorch
SQL
Tensorflow
Natural Language Processing (NLP)
Kubernetes
Python
CategoriesNew
AI & Machine Learning
Requirements
  • An agile development background and history of working in highly agile environments
  • Strong communication, collaboration, and problem-solving skills
  • A great human who contributes to an amazing, accepting, and diverse culture
  • 5+ years of experience with most of the following skill sets and technologies: Python, containerization ecosystems (Docker, Kubernetes, etc) DAG management systems (Airflow, Argo, Prefect, etc) SQL/NoSQL database design and optimization, AWS, Unit tests, CI/CD tools, documenting software architectures, refactoring existing codebases
  • Proficiency with a deep learning framework such as Pytorch or Keras or Tensorflow or OpenAI
  • Proficiency with Python and basic libraries for machine learning such as scikit-learn and pandas
  • Expertise in visualizing and manipulating big datasets
  • Familiarity with Linux
  • Ability to select hardware to run an ML model with the required latency
  • Full suite of health insurance options, in addition to generous paid time off
  • Pre-planned company-wide wellness holidays
  • Retirement options
  • Health & charitable donation stipends
  • Impactful Business Resource Groups
  • Flexible work hours & the opportunity to work from anywhere
  • The opportunity to work with leading biotech and life sciences companies in an innovative industry with a mission to improve healthcare around the globe
Responsibilities
  • Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress
  • Managing available resources such as hardware, data, and personnel so that deadlines are met
  • Analyzing the ML algorithms that could be used to solve a given problem and ranking them by their success probability
  • Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world
  • Verifying data quality, and/or ensuring it via data cleaning
  • Supervising the data acquisition process if more data is needed
  • Finding available datasets online that could be used for training
  • Defining validation strategies
  • Defining the preprocessing or feature engineering to be done on a given dataset
  • Defining data augmentation pipelines
  • Training models and tuning their hyperparameters
  • Analyzing the errors of the model and designing strategies to overcome them
  • Deploying models to production
Desired Qualifications
  • Understanding of LLM, Langchain framework is a plus