Senior Machine Learning Engineer (NLP) | £60k - £100k DOE

at  Robert Walters

My client is on a mission to democratize wealth management through their AI-powered Co-pilot assistant for financial advisors. By automating complex workflows, they empower financial advisors to spend more time building genuine connections with clients. They embrace experimentation, fast execution, and continuous learning. They value driven individuals who take initiative, voice their opinions, and thrive as self-starters.

REQUIREMENTS

The ideal candidate will possess

• Strong expertise in NLP, including experience building, deploying, and optimizing NLP systems.
• At least 5 years of hands-on experience in Machine Learning concepts such as deep learning and reinforcement learning.
• Hands-on experience with fine-tuning LLMs like Llama-2 or SMoE language models like Mixtral using reinforcement learning.
• Minimum 3 years of experience using AWS services in machine learning (Sagemaker, Lambda, etc).
• Exceptional coding abilities in Python and strong proficiency in machine learning libraries.
• A self-starter and entrepreneurial mindset, proactively identifying weaknesses in the product and diving into solutions.
• Comfort in a fast-paced tech startup environment with a high degree of uncertainty and experimentation.

If you’re passionate about revolutionizing wealth management through AI and thrive in a dynamic and innovative environment, I encourage you to apply.

As a Senior Machine Learning Engineer specializing in NLP, you will

• Develop and enhance NLP/ML models for document processing, information extraction, question answering, report generation, and other applications.
• Collaborate with the Engineering team, CTO, and CEO to iterate on new features.
• Implement transformer models, LLMs, statistical NLP algorithms, and information retrieval techniques using Python, PyTorch, TensorFlow, NumPy, scikit-learn, etc.
• Work on multimodal transformers (LayoutMLv3) and VDU models like Donut.
• Develop RAG models with embeddings and continue to fine-tune them through reinforcement learning.
• Collaborate cross-functionally in a matrixed environment to bring cutting-edge AI solutions to life.