City
Remote
Category
Engineering
Job Level
Mid-senior
Employment Type
Full-time
Job Description
Are you excited to work on an AI startup with a disruptive product that is used by several startups backed by Tier I VCs. Fractionalize adds a cognitive layer on top of a company's tech stack. Fractionalize founders include Capital One and E*Trade alums and serial entrepreneurs whose last venture was backed by Techstars and several well-known VCs. We recently sold our startup and launched Fractionalize.
In our last venture, we witnessed firsthand how startups and small businesses struggled with managing business functions. While our customers did a great job at innovation they simply lacked the technical know-how of managing business functions for growth, operation, legal, and compliance.
Our launch product helps startups with managing growth function. We launched recently and have already signed up our first set of customers. We are looking for a machine learning engineer to lead a major part of the platform as we expand. This is a senior role in the engineering team.
Job Responsibilities
- End-to-End Model Development:
- Design, develop, and deploy machine learning models, from research to production.
- Handle the entire pipeline from data collection, data preprocessing, feature engineering, and model training to validation and deployment.
- Data Pipeline Management:
- Build and maintain scalable data pipelines for collecting, transforming, and preparing data for model development.
- Ensure data quality and availability, optimizing data processing frameworks for efficiency.
- Model Deployment & Scaling:
- Deploy machine learning models in production environments, using best practices like model versioning and A/B testing.
- Work on scaling models in real-time production systems, ensuring high availability and low latency.
- Model Monitoring & Maintenance:
- Monitor models in production to ensure performance stability.
- Implement systems for alerting and handling model drift, retraining, and continuous improvement.
- ML Infrastructure Management:
- Develop and optimize ML infrastructure, including managing model training environments, experiment tracking, and resource management.
- Leverage cloud technologies (AWS, GCP, Azure) for distributed model training and deployment.
- Collaboration with Cross-Functional Teams:
- Work closely with data scientists, software engineers, and DevOps teams to integrate ML solutions into products.
- Collaborate with business stakeholders to understand requirements and ensure models align with business goals.
- Algorithm Selection & Optimization:
- Choose the right algorithms and models based on the business problem, balancing accuracy, performance, and explainability.
- Optimize models for speed, scalability, and performance, focusing on interpretability and robustness.
- Research & Innovation:
- Stay updated with the latest research in ML/AI, testing new methods and algorithms.
- Propose and lead initiatives for integrating cutting-edge machine learning techniques into existing or new solutions.
- Compliance & Ethical Considerations:
- Ensure all models and solutions comply with relevant regulations and ethical guidelines, especially in sensitive domains like healthcare, finance, or privacy.
- Documentation & Reporting:
- Document model design, architecture, training procedures, and maintenance steps.
- Create regular reports on model performance, areas for improvement, and roadmap for future iterations.
Job Requirements
Technical Skills:
- Strong Programming Skills:
- Proficiency in Python (must-have) and additional languages like Go or Java is a plus.
- Familiarity with key libraries and frameworks like TensorFlow, PyTorch, Scikit-learn, and XGBoost.
- Experience with ML Lifecycle Management Tools:
- Hands-on experience with tools like MLflow, Kubeflow, or Airflow for managing experiments, model training, and deployment pipelines.
- Cloud Experience:
- Experience in deploying and scaling models on cloud platforms like AWS, GCP, or Azure, with expertise in using services like SageMaker, GCP AI Platform, etc.
- Knowledge of Data Engineering:
- Solid understanding of data engineering concepts, including ETL pipelines, distributed systems (e.g., Apache Spark), and databases (SQL/NoSQL).
- Mathematics & Statistics:
- Strong grasp of linear algebra, probability, statistics, and optimization techniques used in machine learning algorithms.
- Model Deployment & MLOps:
- Familiarity with containerization (Docker) and orchestration (Kubernetes) for deploying models in production.
- Strong understanding of Continuous Integration/Continuous Deployment (CI/CD) for ML pipelines.
- Version Control & Experiment Tracking:
- Experience with Git for version control and tools like DVC (Data Version Control) for managing datasets and models.
Soft Skills:
- Problem-Solving:
- Strong analytical skills to translate complex business problems into ML solutions.
- Collaboration & Communication:
- Ability to explain complex technical details to non-technical stakeholders and collaborate across teams.
- Attention to Detail:
- Thoroughness in model validation, error analysis, and performance monitoring.
Educational Background:
- Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or a related field. PhD is a plus but not always required.
- Solid foundation in machine learning algorithms, statistical analysis, and optimization techniques.
Experience:
- 3-5+ years of experience in building, deploying, and maintaining machine learning models in a production environment.
- Experience with owning the entire MLLC is critical.
Bonus Skills:
- Experience with AutoML for automating parts of the pipeline.
- Familiarity with Decision models or predictive analytics.
- Domain-specific expertise, such as NLP, computer vision, or recommendation systems.