Job Description
Join the Future of Intelligence at Nexus 2026
Nexus 2026 is at the forefront of next-generation artificial intelligence, engineering solutions that redefine human-machine interaction. We are seeking a visionary Senior Machine Learning Engineer to join our elite technical team and help architect the AI infrastructure for the year 2026 and beyond.
In this role, you will not just implement models; you will pioneer algorithms that solve complex, unsolved problems. You will work in a collaborative, high-performance environment that values innovation, speed, and ethical AI development. If you are passionate about pushing the boundaries of what is possible with neural networks and large language models, this is your opportunity to lead.
Why Nexus 2026?
- Work with cutting-edge hardware and cloud-native infrastructure.
- Competitive equity package and performance bonuses.
- Unlimited PTO and comprehensive health benefits.
- Access to the latest research papers and conferences.
Responsibilities
- Design, develop, and deploy scalable machine learning models and deep neural networks that drive core product features.
- Optimize existing ML pipelines for latency, throughput, and resource efficiency in production environments.
- Collaborate with cross-functional teams of data scientists, engineers, and product managers to define AI requirements and translate them into technical solutions.
- Conduct rigorous testing, validation, and monitoring of model performance to ensure accuracy and reliability over time.
- Stay abreast of the latest advancements in AI research (e.g., Transformers, Diffusion Models) and integrate relevant breakthroughs into our architecture.
- Lead code reviews and mentor junior engineers, fostering a culture of technical excellence and continuous learning.
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related technical field (PhD preferred).
- 5+ years of professional experience in machine learning, deep learning, or natural language processing.
- Proficiency in Python, PyTorch, TensorFlow, or JAX.
- Strong understanding of distributed systems, cloud platforms (AWS, GCP, or Azure), and containerization (Docker/Kubernetes).
- Experience with MLOps tools and frameworks (MLflow, Kubeflow, Airflow).
- Proven track record of shipping high-impact ML products to production.
- Excellent problem-solving skills and ability to thrive in a fast-paced, agile environment.