Machine Learning Engineer - Computer Vision & Deep Learning

About the Company

We are a dynamic technology enterprise focused on creating advanced AI and computer vision solutions deployed in complex real-world scenarios. With an established global product, strong adoption, and a seasoned engineering team, we are rapidly expanding our machine learning capabilities during international growth.

About the Role

This role seeks a hands-on Machine Learning Engineer who moves beyond assembling off-the-shelf AI APIs to designing, training, optimizing, and deploying ML models into production. The successful candidate will work closely with product and software teams, developing novel ML functionalities primarily within computer vision and deep learning disciplines.

Responsibilities

• Collaborate with product, software, and ML teams to create machine learning features from inception.
• Focus heavily on computer vision and deep learning tasks using extensive visual datasets.
• Engage in the end-to-end ML workflow: model design and development, training pipeline creation, data sourcing and labeling, experimentation, and performance evaluation.
• Enhance model accuracy, reduce latency, and optimize inference execution.
• Transition models from prototype to deployment.
• Implement software tools surrounding the ML lifecycle including versioning, deployment, and monitoring.
• Support both cloud-based and edge inference applications.
• Explore advanced areas such as large language models, multimodal architectures, and generative AI when they align with product improvements.

Qualifications

• Approximately 4–8 years of applicable machine learning engineering experience, with flexibility for exceptional candidates.
• Deep domain expertise in computer vision with a track record of developing effective image-based ML solutions.
• Robust knowledge of modern deep learning techniques beyond traditional ML approaches.
• Proficient in building and training models using PyTorch or comparable frameworks.
• Proven ownership of full custom model lifecycle: development, experimentation, evaluation, optimization, and production deployment.
• Strong software engineering skills, comfortable with writing production-level code and integrating ML into comprehensive systems.
• Experience with production ML concerns such as pipelines, version control, deployment, monitoring, and performance fine-tuning.

Highly Relevant Backgrounds

• Experience sectors include robotics, autonomous systems, industrial AI, smart cameras, medical imaging, satellite imagery, defense tech, IoT, physical AI, image recognition, video analytics, edge AI, and multimodal AI.
• Particularly valued is experience deploying ML models onto edge or resource-limited devices.

Role Clarifications

• This is not a conventional data scientist role focused on analytics, dashboards, forecasting, or statistical modeling.
• It is not solely an MLOps role nor a pure academic research position.
• It's distinct from AI engineer roles centered mainly on using pre-built AI APIs or foundation models.
• While generative AI and related models are part of the strategic vision, the core of this role centers on rigorous machine learning engineering, computer vision, and deep learning.

Culture and Candidate Fit

• Ideal candidates thrive in startups or smaller teams where ownership over complete problems is expected.
• Comfort with ambiguity, fundamental problem-solving, and transitioning fluidly between experimentation and robust engineering are key.
• An inquisitive mindset about model behavior and outcomes is highly valued.

Compensation and Opportunity

• Join a profitable, expanding tech company with a well-established international product.
• Benefit from institutional investment, excellent client retention, and global growth phases.
• Work within a compact ML team with substantial influence on AI architecture, models, tooling, and strategic direction.
• Opportunity to evolve work into advanced computer vision, edge AI, multimodal, and generative AI domains over time.