Surge Global is a digital consultancy that leverages marketing, data, and technology to help businesses grow. As Sri Lanka’s leading digital firm, we employ the best content, creative, design & engineering talent the country has to offer.

Embracing a culture of transparency and equality in the workplace, we’ve built an environment helping Sri Lanka’s best marketing, creative, data and engineering talent thrive. Our team looks at untangling multivariate problems with an open mind and the willingness to constantly test and learn.

With our headquarters in Colombo Sri Lanka, we help scale startups to Fortune 500 companies across the United States, United Kingdom, Australia, and Sri Lanka.

We are looking for an experienced Technical Lead in Machine Learning to join our company's newly established engineering team. As a Technical Lead, you will oversee and be responsible for technical teams and the projects they undertake.

Responsibilities:

• Manage a Machine Learning (ML) engineering team, and taking total ownership of technical components of projects relating to ML and data.
• Develop ML solutions that fit into business processes by explicating data needs and interfaces with existing systems and leading/consulting on implementation of data pipelines, sizing compute/storage needs, devising validation strategies and metrics, leading implementation and deployment efforts, reporting intelligibly on results and conferring with product managers/testers/users for feedback for agile iteration.
• Propose or constructively critique proposed designs, technology decisions, or other architectural aspects for ML systems and components based on business requirements by translating them to machine learning problems and technical specifications.
• Collaborate with product managers, software/data engineering leads and clients to drive successful delivery of ML solutions through timely and effective milestone setting and estimation, and risk/contingency identification.
• Define research agendas for novel problem areas, lead teams in their pursuit through well-planned research, experimentation and review phases, and leverage successful outcomes in projects.
• Lead scaling of research prototypes into production-grade systems with continual learning, monitoring and MLOps considerations addressed.
• Review code for quality, extensibility, appropriate abstraction and tests.
• Review documentation, administer and review version control, CI/CD and MLOps setups, and both ensure adherence to best practices and organizational standards, and guide team members on the same.
• Manage communications effectively from standups with the team to status updates for leadership/clients at different levels of detail for different audiences of stakeholders, internal and external.
• Interview and make recruitment decisions, and onboard new engineers.
• Run 1-on-1s for the team and help team members improve, including through training engineers and building training procedures, and streamlining processes.

Requirements:

• Bachelor's/Master's degree or higher in Computer Science, ML, Mathematics, Statistics or a related field (or equivalent relevant work experience).
• 5+ years of experience working as an ML Engineer, Data Scientist or Software Engineer, with 3+ years in a lead/senior ML productization or research role managing a team of engineers to deliver on an ML-centric project.
• Extensive experience working on and contributing to design of large-scale, data-intensive software systems, leading and supporting multiple work streams covering research and experimentation as well as production level pipelines.
• Concrete understanding of the full ML lifecycle from data generation through design, development and deployment, with a proven track record of contributing to to functional and non-functional (e.g., performance, scaling) aspects of ML projects.
• Outstanding problem-solving skills backed by a strong technical grasp of ML and deep learning, from core theory to implementing research papers from scratch in at least one major frameworks like PyTorch, JAX or Tensorflow.
• Expert proficiency in Python with mastery of OOP design principles/patterns, and with its data science stack; advanced skills in SQL, PySpark, Julia/R are advantageous.
• Demonstrable experience with deployment on the cloud with containerisation/orchestration technologies (e.g., Docker, Kubernetes), distributed/cluster computing (e.g., Spark, Ray) and with model serving, CI/CD and MLOps tools; experience with ML stacks on major clouds such as AWS, Azure, GCP or with data lakes is advantageous.
• Ability to keep up to date with research trends, define research agendas and lead them whilst delivering on realistic roadmaps.
• Excellent leadership and interpersonal skills with the ability to communicate effectively at different levels of detail based on the audience, and the ability to mentor and guide junior team members.
• Excellent communication skills and the ability to collaborate with cross-functional teams.