Braintrust (201-500 Employees, 255% 2 Yr Employee Growth Rate)
Braintrust is the first decentralized Web3 talent network that connects skilled, vetted knowledge workers with the world’s leading companies. The community that relies on Braintrust to find work are the same people who own and build it, ensuring the network always serves the needs of its users, instead of a centrally-controlled corporation. And because the community of knowledge workers and contributors earns ownership and control of Braintrust through its native BTRST token for their contributions to the network and its growth, new Talent and jobs have participated in the network at record speeds.
Braintrust has over 700,000+ community members, with knowledge workers and project contributors across the world. Braintrust is trusted by hundreds of Fortune 1000 global enterprises including Nestlé, Porsche, Atlassian, Goldman Sachs, and Nike. For more information, visit: www.braintrust.com.
BTRST is available on Coinbase.com and in the Coinbase Android and iOS apps. Coinbase customers can trade, send, receive, or store BTRST in most Coinbase-supported regions. For more information on Braintrust and the BTRST token, read the “Braintrust: The Decentralized Talent Network” whitepaper.
- JOB TYPE: Freelance, Contract Position (no agencies/C2C – see notes below)
- LOCATION: Global – Remote (TimeZone: PST/CIST | Partial overlap)
- HOURLY RANGE: Our client is looking to pay $50 – $80/hr
- ESTIMATED DURATION: 40hr/week – long-term
You will be working in a fast-moving and growing company; applicants should be self-starting and comfortable learning and using new technologies, systems, and processes.
Our client is a well-funded and highly disruptive SaaS platform company that enables innovative companies like Salesforce, Palo Alto Networks, Snowflake, Databricks, HPE Aruba Networks and Qlik to transform their Voice of the Customer (VoC) programs and harness true customer sentiment signals in real-time to proactively improve customer relationships, products and operations while decreasing churn and top-line revenue leakage. We hire big picture thinkers who can simultaneously roll up their sleeves and deliver with measurable impact. We dream in years, plan in months and execute in days. Our culture is honest, fast paced, collaborative and very down to earth.
How your work will support their growth:
The mission of Data Science team is to create cutting-edge machine learning (ML) models that can extract new signals from unstructured data and make insightful, actionable predictions for our customers.
The work you’ll do:
- Ship – Increase velocity of ML model deployment into production through automation of model management, deployment, and rollout processes.
- Validate – Increase confidence of model rollouts by enriching and automating model validation prior to and immediately after deployment.
- Measure – Provide insight into accuracy and relevance of ML model predictions in production by measuring and monitoring model input and output data distributions, as well as user engagement/feedback on predictions.
- Automate – Incorporate user feedback/activity into new ML model training by automation of data collection, model retraining, model measurement, etc., towards a goal of continuous automated model retraining.
- Build – Provide internal tools or incorporate commercial tools (e.g., ClearML) into data scientist workflows for data analysis, feature generation, model development, etc., to boost ML team productivity.
- Collaborate – Bridge the gap between ML research and production-grade backend code by working with other engineering teams to integrate new ML models or APIs into production.
- A self-starter, with the interest and passion to contribute in a fast-paced startup environment.
- B.S., degree or equivalent in Computer Science or similar field of study.
- 5 years of experience building ML products
- Professional experience as a machine learning engineer or a strong backend engineer with a passion on machine learning
- Strong proficiency in software development and distributed systems
- Fluent in building APIs in Python, particularly in FastAPI or Flask.
- Understanding and use of Pytest, Docker, and sqlalchemy.
- Experienced with ML model management, deployment, and/or versioning.
- Experienced with database and data warehouse.
- Understanding and use of common Python data science libraries such as Pandas, numpy, and scikit-learn.
- Experienced with common Python deep learning libraries such as PyTorch and TensorFlow.
- Experienced with cloud platforms (AWS, GCP, Azure).
- Experienced with MLops platforms such as Kubeflow or MLFlow.