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Machine Learning Engineering Director
2 months ago
causaLens is a pioneering company in the field of Causal AI, a new category of intelligent machines that understand cause and effect. Our mission is to build truly intelligent machines that can unlock the true potential of AI for the enterprise.
Job DescriptionWe are seeking a highly motivated and experienced Lead Machine Learning Engineer to join our Product team in London. In this leadership role, you will spearhead the development of our Causal AI platform, designed to optimize business processes across diverse industries.
Key Responsibilities- Provide technical leadership in the development of our Causal AI platform, guiding both the vision and execution within our Product Engineering team.
- Lead the design and development of advanced Causal AI algorithms, with a focus on time series and tabular data, ensuring they are optimized for scalability and performance.
- Oversee feature engineering and machine learning initiatives to deliver robust, production-quality solutions.
- Provide mentoring and technical guidance to junior engineers and data scientists, fostering a culture of continuous learning and innovation.
- Collaborate with cross-functional teams, including product management, DevOps, and UX/UI design, to seamlessly integrate Causal AI capabilities into our platform's architecture.
- A minimum of 5 years of experience in machine learning engineering or a related field, with demonstrated success in deploying machine learning models into production environments.
- Strong academic background in a quantitative discipline (e.g., machine learning, statistics, mathematics) or equivalent industry experience.
- Expertise in translating complex machine learning algorithms into scalable, production-quality code, with proficiency in Python and a strong understanding of optimization techniques (experience with Cython and CUDA is a plus).
- Experience in developing Large Language Models (LLMs) is advantageous.
- In-depth understanding of computer architecture and its implications on AI/ML performance.
- Comprehensive knowledge of the software development life cycle, including best practices in version control, testing, and deployment.
- A competitive salary and benefits package.
- A hybrid working setup and a dedicated team.
- Access to mental health support through Spill.
- A referral bonus program.
- A cycle to work scheme.
- Office snacks and drinks.
Our interview process consists of a few screening interviews and a 'Day 0' which is spent with the team.