Job Description
Shape the Future of Intelligent Systems
Apex Digital Systems is on a mission to revolutionize how businesses interact with data. We are seeking a visionary Senior AI Engineer to join our elite engineering team in San Francisco. In this role, you won't just write code; you will architect the neural architectures that power our next-generation products. If you are passionate about pushing the boundaries of machine learning and eager to work in a fast-paced, high-impact environment, we want to hear from you.
Why Join Apex?
- Competitive Compensation: $160k - $220k base salary with equity options.
- Flexible Work: Hybrid model supporting San Francisco and remote talent.
- Growth: Work on bleeding-edge technologies including Large Language Models (LLMs) and Computer Vision.
- Perks: Unlimited PTO, comprehensive health coverage, and annual learning stipends.
Your Impact
As a Senior AI Engineer, you will lead the design and implementation of scalable machine learning solutions that drive key business metrics. You will collaborate directly with product managers and data scientists to translate complex requirements into robust, production-ready algorithms.
Responsibilities
- Design, train, and deploy state-of-the-art machine learning models and deep learning architectures.
- Collaborate with cross-functional teams to integrate AI capabilities into existing software products.
- Mentor junior engineers and data scientists, fostering a culture of technical excellence and innovation.
- Optimize model inference performance and ensure scalability across distributed systems.
- Conduct rigorous A/B testing and model evaluation to validate performance improvements.
- Stay abreast of the latest research in AI/ML and implement cutting-edge techniques.
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related field (PhD preferred).
- 5+ years of professional experience in machine learning, AI, or a related technical field.
- Strong proficiency in Python, PyTorch, TensorFlow, or similar deep learning frameworks.
- Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
- Proven track record of deploying models to production environments with high availability.
- Excellent problem-solving skills and the ability to communicate complex technical concepts to non-technical stakeholders.