Senior Machine Learning Engineer

Position Overview We are looking for a talented and experienced Deep Learning Engineer specializing in Large Language Models (LLMs) to join our dynamic team. In this role, you will play a pivotal part in enhancing the reliability, safety, and performance of AI models and systems. You will work closely with AI researchers and product teams to drive cutting‑edge advancements in AI safety and responsible AI solutions. Responsibilities Assist in designing and implementing end‑to‑end safety‑focused frameworks for LLMs. Develop and apply risk mitigation techniques, including safe inference strategies to ensure reliable AI behavior. Identify vulnerabilities in AI systems and contribute to adversarial testing, bias detection, and mitigation strategies. Collaborate with cross‑functional teams to integrate safety mechanisms into AI workflows and pipelines. Design and optimize LLM architectures to improve performance, scalability, and efficiency. Fine‑tune pre‑trained LLMs on domain‑specific datasets to improve task performance. Stay up to date with the latest research papers, techniques, and advancements in deep learning and related fields. Requirements Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related field. Proven experience in developing and deploying Large Language Models (LLMs), with a focus on architectures such as GPT, BERT, and their variants. Strong programming skills in Python and experience with deep learning frameworks such as TensorFlow or PyTorch. Knowledge of distributed training techniques and experience with cloud computing platforms (e.g., AWS, Azure, Google Cloud). Excellent problem‑solving skills and ability to work independently and in a team environment. Strong communication and collaboration skills. Compensation Targeted compensation guideline: $110,000 - $145,000. Compensation will vary based on number of factors, including market demand for specific skills, role type, job level, and individual qualifications. Final salary offers are determined by considerations including, but not limited to, subject matter expertise, demonstrated skill level, relevant experience, geographic location, education, certifications, and training. #J-18808-Ljbffr