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