Applied Machine Learning Engineer

Requirements 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 (Desirable) Master’s degree in Computer Science, Engineering, or a related technical field (Desirable) Experience working in a startup or fast-paced environment (Desirable) Hands‑on experience fine‑tuning machine learning models, including supervised fine‑tuning (SFT) and reinforcement learning from human feedback (RLHF or RFT) (Desirable) Solid understanding of generative AI, machine learning principles, and enterprise infrastructure What the job involves 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 Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low‑latency inference to scalable model serving Build What’s Next: Work with bleeding‑edge technology that impacts how businesses and developers harness AI globally Ownership & Impact: Join a fast‑growing, passionate team where your work directly shapes the future of AI—no bureaucracy, just results Learn from the Best: Collaborate with world‑class engineers and AI researchers who thrive on curiosity and innovation #J-18808-Ljbffr