Applied Machine Learning Engineer

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.