Machine Learning Engineer

Machine Learning Engineer

📍 Location: Hinjewadi Pune (Onsite)

🕒 Experience: 2+ years

📄 Employment Type: Full-Time

About Skylark Labs At Skylark Labs, we pioneer embodied artificial intelligence that seamlessly integrates into every physical device, evolving toward true general intelligence. Our mission is to transform the world by creating adaptive AI systems that empower innovation, enhance safety, and redefine connectivity for a smarter, more sustainable future. We are building scalable AI-driven solutions to power next-generation visual intelligence systems. Join our team of passionate engineers and researchers to solve real-world computer vision problems across domains like robotics, smart cities, manufacturing, surveillance, and public safety - all while contributing to the future of embodied AI.

Key Responsibilities

• Design, develop, and deploy robust computer vision models for object detection, classification, segmentation, pose estimation, tracking, and OCR tasks
• Handle end-to-end data management including dataset curation, preprocessing, augmentation, and annotation for large-scale image and video data
• Implement optimization techniques such as model quantization, pruning, and knowledge distillation for efficient edge deployment
• Optimize model inference performance on both cloud and edge computing platforms
• Develop and fine-tune deep learning architectures using modern frameworks
• Integrate computer vision models into production pipelines and edge-based inference systems
• Collaborate with MLOps/DevOps and product teams to deploy CV solutions at scale
• Research and evaluate emerging techniques to continuously improve system accuracy and efficiency

Required Skills

• 2+ years of hands-on experience in computer vision and deep learning development
• Proficiency in Python with computer vision libraries and data processing tools
• Strong expertise in deep learning frameworks for training custom models and leveraging pretrained architectures
• Experience with deployment and optimization on edge computing devices
• Familiarity with hardware acceleration and model optimization tools
• Understanding of classical computer vision techniques including image processing fundamentals
• Experience in data pipeline management for vision applications
• Proficiency with version control systems and collaborative development workflows
• Basic experience working with video streams from cameras, drones, or embedded platforms

Nice-to-Have

• Experience with model serving frameworks and inference optimization tools
• Familiarity with MLOps practices and CI/CD pipeline integration
• Knowledge of Vision-Language Models (VLMs) and multimodal AI systems for advanced visual understanding
• Knowledge of cloud AI services and edge computing platforms
• Experience with real-time object tracking algorithms
• Domain experience in smart surveillance, OCR, safety detection, or embedded CV applications.

Application & Screening Process Interested candidates are requested to complete the screening form using the link below:

🔗 Screening Form: https://docs.google.com/forms/d/e/1FAIpQLSc06RIg92Erj0o1xWl7f550D7ChE1LrFRMpIWhh9R1X5hrLuA/viewform?usp\=sharing\&ouid\=118009209953067933509

• If the link does not open directly, please copy and paste it into a new browser tab.
• Please ensure that all information submitted in the form is accurate and up to date.
• Candidates shortlisted through the screening process will be contacted via email and/or phone call regarding the next stages of the hiring process, including technical assessments and interviews.
• Please apply only if you are available to attend the interview process (online and/or in-person) as required. Once an interview is scheduled, candidates are expected to attend the interview as planned. We kindly request applicants to avoid last-minute cancellations, rescheduling requests, or no-shows unless there is a genuine unavoidable reason.
• Please note that incomplete applications may not be considered for further evaluation.

We appreciate your interest in Skylark Labs and look forward to reviewing your application. Good Luck!

Regards, Skylark Labs Recruitment Team -