Job Description
Join the Vanguard of Innovation.
Apex Future Systems is spearheading Project 2026, an ambitious initiative to redefine the boundaries of artificial intelligence and predictive analytics. We are seeking a visionary Lead Machine Learning Engineer to architect scalable, high-performance models that will power the next generation of enterprise intelligence.
In this pivotal role, you will not just write code; you will shape the strategic direction of our AI infrastructure. You will work in a collaborative, high-velocity environment alongside world-class data scientists, researchers, and engineers. If you are driven by the challenge of solving complex problems and are ready to leave a legacy in the tech landscape of the future, this is your opportunity.
Why Join Us?
- Work on cutting-edge, future-forward technology.
- Competitive compensation and equity package.
- Flexible remote-first culture with premium NYC offices.
Responsibilities
- Architect End-to-End ML Pipelines: Design, build, and deploy robust machine learning models and MLOps infrastructure from scratch.
- Strategic Leadership: Mentor a team of junior engineers and data scientists, fostering a culture of technical excellence and innovation.
- Algorithm Development: Research and implement novel algorithms to optimize predictive accuracy and reduce latency.
- System Optimization: Oversee the scaling of data infrastructure to handle petabyte-scale data processing in real-time.
- Cross-Functional Collaboration: Partner with product managers and stakeholders to translate business requirements into technical roadmaps.
- Risk Management: Identify potential biases in data and models to ensure ethical AI deployment.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, Statistics, or a related field.
- Experience: 7+ years of professional experience in machine learning engineering, with at least 3 years in a lead or senior capacity.
- Technical Stack: Proficiency in Python (PyTorch, TensorFlow), SQL, and distributed computing frameworks (Spark, Kafka).
- Cloud Mastery: Deep expertise in cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
- Problem Solving: Proven track record of solving complex, unstructured problems with data-driven solutions.
- Communication: Exceptional ability to communicate complex technical concepts to non-technical stakeholders.