Member of Technical Staff (Machine Learning Engineer)
Role Overview
As a Machine Learning Engineer at Reka AI, you will translate cutting-edge research into production-ready ML systems, designing and deploying end-to-end models for image and video processing. You will own the full ML lifecycle, from experimentation to deployment, collaborating with researchers and cross-functional teams to scale solutions. This senior-level role requires a strong research background and experience with cloud deployment and low-latency systems.
Perks & Benefits
This is a fully remote position, allowing you to work from anywhere. You'll join a research-driven team at the forefront of AI, with opportunities to contribute to top-tier conferences and open-source projects. The role offers significant impact in building scalable ML systems and potential for career growth in a dynamic startup environment.
Full Job Description
What You’ll Do
Translate cutting-edge research into production-ready machine learning systems
Design, build, and deploy end-to-end ML models and pipelines
Develop and optimize models for image and video processing
Own the full ML lifecycle: experimentation, training/fine-tuning, evaluation, and deployment
Rapidly prototype using open-source models and adapt them for product needs
Conduct experiments, analyze results, and iterate to improve performance
Collaborate with researchers and cross-functional teams (product, engineering, design) to deliver ML solutions at scale
Participate with advancements in machine learning and apply them to continuously improve products
What We’re Looking For
Required QualificationsMS/PhD in Computer Science, Electrical Engineering, or related field
Strong research experience with familiarity in top conferences (e.g., CVPR, ICCV, NeurIPS)
5+ years of experience in Python and proficiency in Java, C++, or Scala
Strong understanding of diffusion models
Strong understanding of multi-threading and memory management
Solid knowledge of ML architectures: CNNs and Transformers
Experience with PyTorch or TensorFlow
Experience building end-to-end ML deployment and inference systems, especially for low-latency, real-time applications
Experience deploying ML models in cloud environments (AWS preferred)
Experience with experiment tracking systems and ML workflows
Nice to HaveExperience in low level optimisation, cuda etc.
Experience productionizing and scaling ML models in real-world systems
Contributions to open-source projects
Experience with MLOps tools or distributed training systems
Familiarity with relational databases (Postgres/MySQL)
Experience handling large-scale data using tools like Spark
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