Job Description
Join the race to Artificial General Intelligence (AGI). At FutureMind Systems, we are not just building software; we are architecting the future. We are seeking a visionary Senior AI/ML Engineer to join our elite research division. Our goal is to achieve breakthroughs in reasoning, planning, and multimodal understanding by 2026. If you are passionate about the frontier of machine learning and want to work with state-of-the-art models, this is your opportunity.
As a key member of our team, you will bridge the gap between theoretical research and scalable production systems. You will work in a high-velocity environment where your code impacts billions of users globally. We offer competitive compensation, equity packages, and the chance to define the next generation of AI assistants.
Responsibilities
- Design and implement novel Large Language Models (LLMs) and Reinforcement Learning from Human Feedback (RLHF) pipelines to achieve human-level reasoning.
- Optimize model inference latency and throughput for real-time, autonomous agent applications.
- Collaborate with cross-functional teams of researchers, engineers, and product managers to translate AGI concepts into deployable features.
- Conduct rigorous experimental analysis to evaluate model performance, bias, and safety metrics against our 2026 roadmap.
- Lead the architecture of scalable MLOps infrastructure ensuring data integrity and model versioning.
- Stay ahead of the curve in academic literature, adapting cutting-edge techniques (e.g., Transformers, Diffusion models) into our proprietary stack.
Qualifications
- PhD or Master’s degree in Computer Science, Mathematics, or a related field, with a focus on AI/ML.
- 5+ years of professional experience in Deep Learning, Natural Language Processing (NLP), or Computer Vision.
- Proven expertise in training, fine-tuning, and deploying LLMs (e.g., GPT, Llama, Claude) using frameworks like PyTorch or TensorFlow.
- Strong programming skills in Python, C++, and experience with distributed computing systems (Ray, Kubernetes, Docker).
- Deep understanding of optimization techniques, quantization, and pruning for edge deployment.
- Experience with prompt engineering, semantic search, and RAG (Retrieval-Augmented Generation) architectures.