Role OverviewWe are seeking a
Lead Machine Learning Engineer to serve as a technical authority and driver of our machine learning systems. In this role, you will lead the architecture, design, and productionization of complex ML models and data pipelines that directly impact core product capabilities. You will operate with a high degree of autonomy, solving non-routine technical challenges, setting engineering standards, and providing technical leadership and mentorship to peer engineers without formal direct-report administration.
Lead Machine Learning Engineer:Technical Leadership & Architecture- Lead the end-to-end design, architecture, and deployment of production-grade machine learning models and ML infrastructure.
- Translate ambiguous, complex business problems into scalable ML engineering solutions and system specifications.
- Establish MLOps best practices, operational standards, framework selections, and model monitoring/evaluation workflows across the team.
- Evaluate emerging ML technologies, architectures, and tools to determine feasibility and adoption for the organization.
Execution & Hands-on Development- Write clean, maintainable, high-performance code (Python, C++, Scala, or equivalent) to build and optimize ML pipelines and training systems.
- Design robust features, feature stores, and scalable data ingestion systems in collaboration with Data Engineering.
- Drive model optimization, latency reduction, distributed training, and cost-efficient inference at scale.
- Implement proactive monitoring for model drift, data quality, performance degradation, and system health in production.
Cross-Functional Collaboration & Mentorship- Act as the primary technical contact for product managers, domain experts, and executive stakeholders to align ML roadmaps with business goals.
- Mentor and guide P1-P3 level Machine Learning Engineers through design reviews, code reviews, and pair engineering.
- Partner with DevOps, Platform, and Security teams to ensure seamless deployment, governance, and infrastructure security.
Requirements- Education & Experience: Bachelor's degree in Computer Science, Machine Learning, Data Science, or related quantitative field with 8+ years of professional ML engineering experience.
- Production ML: Proven track record of taking complex ML models from inception to high-scale production deployment (real-time and batch).
- Frameworks & Tools: Deep expertise with modern ML frameworks (e.g., PyTorch, TensorFlow, Jax) and traditional ML libraries (e.g., Scikit-Learn, XGBoost).
- MLOps & Infrastructure: Hands-on experience with orchestration, containerization, and MLOps platforms (e.g., Kubernetes, Docker, MLflow, Kubeflow, AWS SageMaker, GCP Vertex AI).
- Software Engineering: Strong proficiency in data structures, algorithms, object-oriented design, distributed systems, and CI/CD automation.
About you as the Lead Machine Learning Engineer: - Prior experience acting as a Technical Lead or Principal Contributor on high-impact engineering initiatives.
- Expertise in LLM orchestration, fine-tuning, retrieval-augmented generation (RAG), or modern generative AI systems.
- Familiarity with distributed computing engines (e.g., Ray, Spark, Dask) and high-throughput databases (vector databases, feature stores).
Headquartered in Austin, Texas, DT operates globally across North America, EMEA, APAC, and LATAM.
