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Sr. AI Research Scientist

This role is for one of the Weekday's clients

Salary range: Rs 2800000 - Rs 6000000 (ie INR 28-60 LPA)

Min Experience: 4 years

Location: Bengaluru

JobType: full-time

We are looking for a highly skilled Senior AI Research Scientist with deep expertise in NLP, Generative AI, and ML system design. The ideal candidate will have a strong research background, experience in developing and fine-tuning machine learning models, and the ability to translate cutting-edge AI advancements into production-ready solutions.

Key Responsibilities:

Research & Model Development:

  • Fine-tune and develop machine learning models from scratch.
  • Read and analyze new AI research papers, extracting insights and deploying them in suitable testing environments.
  • Design, evaluate, and implement custom testing and evaluation frameworks for different AI models.
  • Work with agentic frameworks and develop Proof of Concepts (PoCs) around them.

ML Engineering Responsibilities:

  • ML System Development: Design, develop, and maintain scalable and efficient ML systems, including writing ML services and APIs.
  • Model Deployment: Implement and manage the deployment of transformer-based LLMs into production, ensuring reliability and scalability.
  • Infrastructure Management: Optimize and manage the infrastructure supporting ML workflows in collaboration with relevant teams.
  • Data Pipeline Creation: Build robust data pipelines for data collection, processing, and preparation for ML model training.
  • Performance Optimization: Continuously optimize ML models and infrastructure for better efficiency and scalability.
  • Collaboration: Work closely with data scientists, engineers, and cross-functional teams to integrate ML solutions into existing software.
  • Documentation: Develop and maintain comprehensive documentation for ML systems, APIs, and data pipelines.

Backend Engineering Responsibilities:

  • Contribute to product development by integrating ML solutions into real-world applications.
  • Design and model real-world scenarios into code to enhance platform capabilities.
  • Continuously test, optimize, and refine the codebase to improve efficiency and performance.
  • Lead large-scale projects from conceptualization to deployment.
  • Develop an ecosystem of tools and libraries to support robust AI-driven applications.
  • Collaborate with engineers to build a resilient system architecture adaptable to evolving product requirements.
  • Enhance the user experience by working on UI and front-end optimizations.

Ideal Candidate Profile:

  • 5+ years of experience designing multi-component ML-based systems.
  • Strong background in Natural Language Processing (NLP) and experience in AI research-oriented projects.
  • In-depth understanding of Generative AI approaches and methodologies.
  • Hands-on experience with ML frameworks like PyTorch, TensorFlow, and Hugging Face.
  • Proficiency in Python and a strong grasp of software engineering principles.
  • Basic understanding of SQL and database design concepts.
  • Strong knowledge of testing fundamentals and best practices.
  • Excellent communication and collaboration skills.
  • Proven track record in leading and executing technology-driven products.

Bonus Skills:

  • Research publications in the ML domain.
  • Experience with agentic frameworks like AutoGen, LangGraph, CrewAI, etc.
  • Prior experience working on high-volume, always-available web applications.
  • Expertise in cloud platforms such as AWS, GCP, or Azure.
  • Experience deploying ML models in production using Docker and containerization tools.
  • Knowledge of distributed systems and large-scale AI infrastructure.
  • Startup experience is a plus.

Average salary estimate

$52800 / YEARLY (est.)
min
max
$33600K
$72000K

If an employer mentions a salary or salary range on their job, we display it as an "Employer Estimate". If a job has no salary data, Rise displays an estimate if available.

What You Should Know About Sr. AI Research Scientist, Weekday AI

Join our team as a Senior AI Research Scientist at Weekday, where your expertise in natural language processing (NLP), generative AI, and machine learning (ML) system design will be put to the test! We're on the lookout for an individual with a robust research background and proven experience in developing and fine-tuning machine learning models. In this full-time position, you'll be delving into cutting-edge AI research, translating innovative advancements into production-ready solutions. Imagine fine-tuning ML models, designing custom testing frameworks, and developing proof of concepts. You'll also be responsible for building scalable ML systems, deploying transformer-based models into production, and managing robust data pipelines. Collaborating with cross-functional teams, you'll integrate your solutions into real-world applications while continuously optimizing system performance. If you're someone with over 5 years of experience building multi-component ML-based systems and a strong foundation in AI research, this is the opportunity for you! You'll have the chance to enhance infrastructure, lead large-scale projects, and contribute to the creation of a toolset that supports AI-driven applications. With a salary range of INR 28-60 LPA, our goal is to ensure you get the value your skills deserve. If you’re creative, passionate, and ready to make a difference in the AI space, we'd love to hear from you!

Frequently Asked Questions (FAQs) for Sr. AI Research Scientist Role at Weekday AI
What are the key responsibilities of a Senior AI Research Scientist at Weekday?

As a Senior AI Research Scientist at Weekday, you'll engage in a variety of tasks including fine-tuning machine learning models, reading and analyzing new AI research papers, developing and maintaining scalable ML systems, and collaborating with cross-functional teams to implement your ML solutions into real-world applications. Your role will also require you to manage data pipelines and optimize model performance.

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What qualifications are required for the Senior AI Research Scientist role at Weekday?

To qualify for the Senior AI Research Scientist position at Weekday, you should have over 5 years of experience in designing multi-component ML-based systems, expertise in natural language processing, and solid experience with ML frameworks such as PyTorch and TensorFlow. Proficiency in Python, a basic understanding of SQL, and strong collaboration skills are also essential.

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What technologies should a Senior AI Research Scientist at Weekday be familiar with?

In the role of Senior AI Research Scientist at Weekday, familiarity with machine learning libraries like PyTorch, TensorFlow, and Hugging Face is crucial. Additional expertise in cloud platforms such as AWS, GCP, or Azure will be beneficial, particularly for deploying ML models in production scenarios.

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How does a Senior AI Research Scientist collaborate at Weekday?

Collaboration is key for a Senior AI Research Scientist at Weekday, as you'll work closely with data scientists, engineers, and various cross-functional teams to integrate ML solutions into existing software. Effective communication and teamwork will enhance your efforts in developing AI-driven applications.

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What is the salary range for a Senior AI Research Scientist position at Weekday?

The salary range for a Senior AI Research Scientist at Weekday is between INR 28,00,000 to INR 60,00,000 per annum, reflecting the high level of expertise and experience required for this role.

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Common Interview Questions for Sr. AI Research Scientist
What experience do you have with NLP and how have you applied it in machine learning projects?

When answering this question, highlight specific projects where you utilized NLP techniques, describing the challenges faced and the results achieved. Be sure to mention any specific tools or libraries you used, such as NLTK or SpaCy, and discuss how your contributions made a significant impact.

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Can you explain your experience with generative AI and its applications?

Discuss your hands-on experience with generative AI models. Provide examples of projects and describe the methodologies you employed, such as GANs or VAEs. Explain how you validated their output and the challenges encountered during implementation.

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Describe a time when you had to optimize a machine learning model. What steps did you take?

When answering, mention specific metrics you monitored to gauge performance. Discuss the techniques you employed, such as hyperparameter tuning or feature engineering, and provide data-driven results that demonstrate the improvements achieved through your optimization efforts.

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How do you approach reading and analyzing AI research papers?

Outline your method for staying updated with the latest AI research, including how you extract key insights and implement relevant findings into your projects. Mention any tools or frameworks you utilize to track research trends and innovations.

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What is your process for developing scalable ML systems?

Describe your systematic approach to designing and developing scalable ML systems. Focus on architecture, code optimization, and infrastructure management, and explain how you've effectively collaborated with teams to enhance scalability in previous projects.

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How do you document your machine learning projects and systems?

Discuss the importance of documentation in your workflow. Explain how you create comprehensive documentation for models, APIs, and workflows, and mention tools or systems you've used to maintain clarity and accessibility for team members.

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What backend technologies have you integrated with ML solutions in past projects?

Share specific backend technologies you've worked with, including any frameworks and databases. Explain how you've integrated ML solutions into these systems, along with the benefits realized from these integrations for enhancing application performance.

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How do you handle collaboration within cross-functional teams?

Discuss your strategies for fostering collaboration within cross-functional teams. Mention specific communication tools and methods used to ensure smooth interactions and successful integration of ML solutions into broader projects.

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What experience do you have with deploying ML models in production?

Detail your experience with deploying models, highlighting strategies used to maintain reliability and performance. Discuss any deployment tools you've used, such as Docker, and how you monitored the models in production for ongoing evaluation.

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Can you give an example of a leadership role you took in a tech project?

Provide a specific example where you led a significant project, describing your leadership style, how you managed team dynamics, and the outcomes of the project. Highlight the skills that made you successful in guiding the team towards achieving project goals.

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Full-time, remote
DATE POSTED
March 29, 2025

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