Tech Stack
PythonPyTorchScikit-LearnTensorflow
About the role
- Design, build, and ship production-grade ML models for high-impact use cases
- Lead model architecture, data pipeline design, and deployment strategies
- Collaborate across product, engineering, and data teams to drive AI initiatives
- Own the end-to-end ML lifecycle — from data prep to deployment and monitoring
- Review code, guide projects, and mentor engineers through technical challenges
- Champion experimentation, continuous learning, and AI best practices
- Interview and evaluate candidates, contributing to technical hiring and team building
- Stay ahead of ML advancements — and bring cutting-edge solutions into production
Requirements
- Bachelor's or Master’s degree in Computer Science, Engineering, or a related field
- Strong proficiency in Python is a must
- 4–6 years of experience in AI/ML engineering with real-world deployment experience
- Deep familiarity with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn)
- Solid understanding of the end-to-end ML lifecycle: data pipelines, training, validation, monitoring
- Experience working on applied AI problems (e.g., recommendation, fraud, risk, NLP, etc.)
- Track record of technical leadership and mentoring engineers
- Excellent communication and collaboration skills
- Must be based in the US and open to working remotely with regional teams
- Competitive salary and performance-based bonuses
- Fully remote and flexible work arrangement from anywhere in the US
- High ownership role with impact visible at a regional scale
- Direct reporting line to Head of AI with exposure to senior leadership
- Opportunity to lead and shape a growing AI engineering team
- Fast-track career growth and learning opportunities in a high-speed environment
- Collaborative, flat culture where ideas move fast and great work gets noticed
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
PythonML frameworksTensorFlowPyTorchScikit-learndata pipelinesmodel architecturedeployment strategiesmonitoringAI best practices
Soft skills
technical leadershipmentoringcommunicationcollaborationexperimentationcontinuous learningteam buildinginterviewingevaluating candidatesguiding projects
Certifications
Bachelor's degreeMaster’s degree