About Hubnest
Hubnest builds vertical specific product engines for industries that require high operational precision. Founded in 2014, the company has spent twelve years embedded in the operational realities of the industries it serves, developing four purpose built products used by organizations across field services, political and civic operations, continuing care, and enterprise security.
AI and ML Engineer
This role develops and deploys the predictive models that power Hubnest's operational products. You will ensure our systems can intelligently process field data and generate real-time insights for our enterprise and civic clients.
What You Will Be Doing
- Architect, build, and deploy end-to-end machine learning pipelines tailored to vertical-specific enterprise requirements.
- Train and fine-tune predictive models using large datasets from field services, continuing care, and security operations.
- Monitor production models for data drift, performance degradation, and anomalies, implementing automated retraining loops.
- Optimize algorithms and deep learning models for high-throughput, low-latency inference in production environments.
- Develop robust data preprocessing and feature engineering workflows to handle complex, real-world operational data.
- Collaborate with product managers to translate client problems into viable machine learning solutions.
- Design and deploy scalable inference APIs using modern cloud infrastructure and containerization.
- Conduct rigorous A/B testing and offline validation to ensure model outputs meet operational precision standards.
- Implement MLOps best practices, including model versioning, automated testing, and CI/CD for machine learning code.
- Mentor software engineers on AI integrations and promote a culture of data-driven development.
- Maintain comprehensive documentation covering model architecture, data lineage, and algorithmic decision-making processes.
- Research and prototype emerging AI and ML techniques to keep Hubnest's product engines current.
What We Are Looking For
- 4+ years of professional experience in machine learning, deep learning, or AI engineering in a production environment.
- Advanced proficiency in Python and standard ML libraries including TensorFlow, PyTorch, Scikit-Learn, and Pandas.
- 3+ years of hands-on experience with MLOps tools and frameworks such as MLflow, Kubeflow, or AWS SageMaker.
- Proven track record of deploying complex machine learning models into live, high-traffic production systems.
- Deep understanding of cloud computing platforms (AWS or GCP) and containerization technologies including Docker and Kubernetes.
- Strong background in SQL and experience working with both relational databases and NoSQL data stores.
- Experience building RESTful APIs or gRPC services for model serving.
- Demonstrated ability to translate complex mathematical and statistical concepts into clean, maintainable, production-ready code.
- Bachelor's or Master's degree in Computer Science, Data Science, Mathematics, or a highly quantitative field.
- Strong analytical problem-solving skills with close attention to detail and data accuracy.
- Excellent written and verbal communication skills to articulate AI concepts to non-technical stakeholders.
What We Offer
- Competitive compensation commensurate with experience
- Comprehensive health, dental, and vision coverage
- Flexible working arrangements
- Learning and development budget
- Paid time off and statutory holidays
Equal Opportunity Employment
Hubnest is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, sex, national origin, age, disability, genetic information, or any other characteristic protected by applicable federal, state, or local law.
How to Apply
Submit your application through our contact page. Select "Career Opportunity" and include the role title in your message. We review every application and will be in touch if there is a fit.