The Role:
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.
Key Responsibilities:
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.
Minimum Qualifications:
Bachelor’s degree in Computer Science, Engineering, or a related technical field.
5+ years of experience in a software engineering role, with a strong preference for customer-facing roles.
Robust coding skills required, preferably with proficiency in Python.
Demonstrated ability to lead and execute complex technical projects with a focus on customer success.
Strong interpersonal and communication skills; ability to thrive in dynamic, cross-functional teams.
Preferred Qualifications:
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.