- 2+ years of experience with a proven record in the ML and data analytics
- Solid programming experience in Python
- Strong English verbal and written communication
- Understanding foundations of Machine Learning (e.g., supervised/unsupervised learning, classification, regression, validation)
- Understanding of NLP fundamentals
- Experience in the development, deployment, optimization, and support of machine learning solutions
- Experience with AWS (SageMaker, Lambda, SQS, SNS, DynamoDB, Step Functions, Batch, ECS, EC2, S3)
- Experience working with large-scale unstructured and structured data sets and databases
- Experience in building REST API services
As a plus
- Experience with Postgres, RDS
- Familiarity with Pinecone, ElasticSearch, DeepLake, or other Vector DB.
- Knowledge of Container Services (Docker, Kubernetes, etc)
- Experience with Terraform
- Exposure to LangChain, OpenSource LLM Models, Prompt Engineering, OpenAI API.
- Experience with ML frameworks and libraries: PyTorch, Scikit-Learn, HuggingFace, fastText, Gensim, numpy, pandas
- Experience with data processing tools and frameworks (Apache Spark, Airflow, etc)
- Familiarity with model optimization and compression techniques (distillation, pruning, quantization, etc)
About Shelf
Shelf is revolutionizing Generative AI by offering organizations a secure way to incorporate large language models to scale their knowledge, boost employee performance, and improve customer satisfaction. Our technology assesses, identifies issues, prioritizes improvements, and monitors content on a continuous basis in order to turn an organization’s knowledge into an infrastructure for Generative AI enablement. Our clients include Fortune 100 leaders across industries and continents, and we are in an exciting period of high growth.
Machine Learning Engineer
The R&D department plays a pivotal role in driving the company to disrupt the market. Our team strives for engineering excellence and agility in developing solutions. We use the latest advances in cloud, NLP, and ML fields to build services used by top enterprise companies and famous brands, including Glovo, HelloFresh, Herbalife, and Harvard Business Review.
As a Machine Learning Engineer, you’ll be responsible for designing and developing systems, training, and serving production-ready ML-driven models to solve the most challenging problems at the time. You have a real chance to impact thousands of users, influence product development, and work alongside experienced engineers and data scientists.
What Shelf Offers:
- Company Stock Options
- Unlimited leaves
- Hardware: MacBook Pro
- Modern technical stack. Develop open-source software
Why Shelf
- According to MarketWatch in November 2022, the global knowledge management market size was valued at US $405 Billion in 2021 and is expected to expand at a CAGR of 18.12% during the forecast period, reaching US $1.1 Trillion by 2027
- Our Leadership Team has deep knowledge management and AI domain expertise and enterprise SaaS background to execute this plan
- We have raised over $60 million in funding and our investors include Tiger Global, Insight Partners, Connecticut Innovations, and others
- Our platform has been consistently rated #1 for overall usability by Gartner Digital Markets, and has received Easiest to Use, Easiest to Admin, and Highest Adoption awards from G2., and product of the year and innovation awards from leading publications, such as CIO Review.
- We have high velocity growth powered by the most innovative product in our category, 3X growth for 3 years in a row
- We now have over 100 employees in multiple U.S. states and European countries, and we have ambitious hiring goals over the next year
,[Build APIs for ML services in a self-sufficient delivery team that ships code to production frequently, Develop technical designs once you’re more familiar with the overall architecture, Work closely with Product Managers to sketch out design requirements given technical constraints in the initial stage of feature development, Be responsible for the features you built and shipped to production, Utilize AWS services to build Infrastructure as a Code using Terraform, Regularly conduct code reviews of your teammates, Contribute to improving overall engineering culture by sharing your unique experience, learnings, best practices, and patterns] Requirements: Data analytics, Python, Machine learning, NLP, AWS, AWS Lambda, Amazon SQS, SNS, AWS DynamoDB, ECS, AWS ECS, AWS EC2, AWS S3, REST API, API, PostgreSQL, Amazon RDS, scikit-learn, Elasticsearch, pandas, Hugging Face, Docker, Kubernetes, Terraform, PyTorch, Apache Spark, Airflow Tools: . Additionally: Small teams, International projects, Free coffee, Free snacks, Free beverages, Startup atmosphere, Modern office.
PostgreSQL scikit-learn quantization Terraform Amazon Web Services (AWS) aws-step-functions Apache Spark amazon-ecs Elasticsearch amazon-dynamodb Docker NumPy Airflow Machine Learning Amazon SageMaker gensim fasttext LangChain Natural language processing (NLP) amazon-s3 Lambdas pandas Python Hugging Face Prompt engineering pruning amazon-sns amazon-sqs Kubernetes OpenAI API Pinecone batch-file amazon-rds PyTorch amazon-ec2