Applied Machine Learning Engineer (Member of Technical Staff)
Posted Updated
Description
- As an Applied Machine Learning Engineer, you will serve as a vital bridge between cutting-edge AI research and practical, real-world applications
- Your work will focus on developing, fine-tuning, and operationalizing machine learning models that drive business value and enhance user experiences
- This is a hands-on engineering role that combines deep technical expertise with a strong customer focus to deliver scalable AI solutions
- Customer Success: Collaborate directly with the GTM team (Account Executives and Solutions Architects) to ensure smooth integration and successful deployment of ML solutions
- Demo / Proof of Concept (PoC): Build and present compelling PoCs that demonstrate the capabilities of our AI technology
- Application Build: Design, develop, and deploy end-to-end AI-powered applications tailored to customer needs
- Platform Features / Bug Fixes: Contribute to the internal ML platform, including adding features and resolving issues
- New Model Enablements: Integrate and enable new machine learning models into the existing platform or client environments
- Performance Optimizations: Improve system performance, efficiency, and scalability of deployed models and applications
- Partnership Enablement: Work closely with partners to enable joint AI solutions and ensure seamless collaboration
- 5+ years of experience in a software engineering role, with a strong preference for customer-facing roles
- Strong interpersonal and communication skills; ability to thrive in dynamic, cross-functional teams
- Bachelor’s degree in Computer Science, Engineering, or a related technical field
- Robust coding skills required, preferably with proficiency in Python
- Demonstrated ability to lead and execute complex technical projects with a focus on customer success
- Master’s degree in Computer Science, Engineering, or a related technical field
- Experience working in a startup or fast-paced environment
- Hands-on experience fine-tuning machine learning models, including supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF or RFT)
- Solid understanding of generative AI, machine learning principles, and enterprise infrastructure