Staff Software Engineer, AI - Hybrid

Octane•New York City, NY

1d•$150,000 - $210,000•Hybrid

About The Position

Octane is unlocking the power of financial products for merchants and consumers. Our cutting-edge technology and innovative financial products empower businesses with more control and flexibility, enabling them to deliver seamless digital experiences, drive customer loyalty, and build long-term value. Octane supports merchants throughout the sales cycle: connecting dealerships with high-intent buyers, driving transparent, fast, and easy closings with award-winning technology, and providing on-going customer care with superior loan servicing. Founded in 2014, Octane supports over 60 OEM partner brands and over 4,000 dealer partners, and has a team of over 600. Visit www.octane.co . At Octane, we’re building innovative technology solutions that power smarter, faster, and more efficient decision-making. We are seeking an accomplished AI/ML Technical Lead to drive the design, development, and deployment of advanced machine learning solutions that scale seamlessly in production environments. In this role, you’ll shape the strategy for how AI is leveraged across the organization, partnering with cross-functional teams to translate business goals into technical solutions that are not only state-of-the-art, but also ethical, secure, and reliable. This position is ideal for a hands-on leader who thrives at the intersection of research and engineering. You will mentor engineers and data scientists, establish best practices for MLOps, and ensure models deliver consistent value long after deployment. With opportunities to explore cutting-edge fields like generative AI, large language models, natural language processing, and computer vision, you’ll play a pivotal role in setting the technical standard for Octane’s AI initiatives while guiding the team toward innovation and measurable impact.

Requirements

Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or related field with 5+ years of professional experience, or Bachelor’s degree with 8+ years of experience in AI/ML engineering.
Proven expertise with leading ML/DL frameworks such as TensorFlow, PyTorch, or JAX, with hands-on experience training and deploying models.
Strong programming proficiency in Python, with additional experience using languages such as C++ or Java for performance-critical applications.
Practical experience with MLOps tools and workflows, including MLflow, Kubeflow, SageMaker, or Vertex AI to streamline model lifecycle management.
Deep understanding of data engineering concepts such as data pipelines, preprocessing, and distributed computing, as well as hands-on experience with cloud platforms (AWS, Azure, or GCP).
Demonstrated success designing, deploying, and optimizing AI/ML systems at scale, including model serving, monitoring, and iteration.
Strong communication and collaboration skills, with the ability to influence technical direction and align diverse stakeholders.

Nice To Haves

Experience with generative AI and large language models (LLMs), including prompt engineering and fine-tuning approaches.
Knowledge of advanced ML domains such as natural language processing (NLP), computer vision, reinforcement learning, or recommendation systems.
Familiarity with containerization and orchestration technologies, including Docker and Kubernetes, to support scalable deployment.
Hands-on experience with infrastructure-as-code (IaC) tools, such as Terraform or Ansible, to enable automated and reproducible infrastructure management.
Contributions to the AI/ML community, such as open-source projects, conference presentations, or peer-reviewed research publications.

Responsibilities

Lead the end-to-end design, development, and deployment of advanced AI/ML systems, ensuring production environments are scalable, efficient, and highly performant.
Collaborate with cross-functional teams to define AI strategy, evaluate use cases, and translate business objectives into actionable technical solutions.
Build, optimize, and maintain machine learning pipelines, covering data ingestion, preprocessing, model training, evaluation, deployment, and ongoing monitoring.
Establish and champion MLOps best practices, including CI/CD for ML workflows, automated testing frameworks, and continuous model performance monitoring to prevent drift.
Serve as a technical mentor and leader, guiding engineers and data scientists on system architecture, coding standards, and efficient development practices.
Research and evaluate emerging AI methodologies, frameworks, and tools, integrating innovative approaches into the organization’s technology stack where applicable.
Ensure AI solutions meet ethical, security, and compliance standards, including model explainability, bias mitigation, and data privacy requirements.
Partner with engineering leadership to set technical strategy, establish architectural guidelines, and define standards that shape the future of AI initiatives at Octane.