Member of Technical Staff (Machine Learning Engineer)

This listing is synced directly from the company ATS.

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 Qualifications

  • MS/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 Have

  • Experience 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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