Staff Machine Learning Engineer (L4)

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Staff Machine Learning Engineer (L4) based in India.

Role Overview

As a Staff Machine Learning Engineer, you will design and build advanced AI/ML capabilities that power intelligent customer engagement and personalized digital experiences. You will help develop systems that enable applications and AI agents to understand customer identities, interaction histories, preferences, and communication contexts. Working at the intersection of applied research, machine learning engineering, and production software, you will turn complex and ambiguous problems into practical, scalable solutions. You will explore state-of-the-art large language models (LLMs), retrieval techniques, AI orchestration, and data-driven personalization to deliver meaningful product innovations. This role offers significant technical ownership, from designing experiments and validating new ideas to deploying enterprise-grade machine learning systems. You will collaborate with a globally distributed team in a fast-paced, remote-first environment that encourages experimentation, technical excellence, and continuous learning.

• Build innovative AI/ML capabilities: Design, develop, and deploy new machine learning functionality that supports intelligent engagement, contextual understanding, and personalized customer experiences across digital communication channels.
• Lead end-to-end technical initiatives: Take ownership of technically ambitious projects, translating early-stage concepts and ambiguous requirements into robust solutions, from initial research and experimentation through production deployment.
• Design and execute experiments: Develop rigorous experiments to evaluate new ideas, test hypotheses, measure performance, and validate potential improvements, delivering actionable results within short development cycles.
• Advance LLM research and implementation: Investigate and apply state-of-the-art techniques in large language models, LLM orchestration, retrieval-augmented generation, and contextual intelligence to solve complex machine learning challenges.
• Develop scalable machine learning systems: Build and optimize production-grade ML services capable of supporting high-volume data processing, streaming workloads, real-time inference, and demanding enterprise requirements.
• Improve personalization and contextual intelligence: Develop capabilities that help applications and AI agents maintain relevant customer context, understand historical interactions, and deliver consistent, personalized experiences.
• Leverage modern AI development tools: Proactively evaluate and integrate emerging AI frameworks, development stacks, and automation tools to accelerate implementation and focus engineering effort on high-value product differentiation.
• Apply strong ML fundamentals: Use statistical machine learning, transformer architectures, predictive modeling, and other relevant techniques to develop effective solutions to complex data and personalization problems.
• Establish engineering best practices: Promote high standards for system architecture, code quality, experimentation, reliability, and maintainability through technical design reviews and engineering guidance.
• Mentor and support engineers: Share expertise, provide constructive feedback, and help teammates strengthen their technical capabilities without relying on formal managerial authority.
• Collaborate across functions: Work closely with product, engineering, and other stakeholders to align technical solutions with business objectives, communicate findings, and ensure successful delivery.
• Continuously explore new technologies: Stay informed about advances in AI, machine learning, and cloud engineering, rapidly acquiring new skills and applying relevant innovations to evolving product requirements.
• Contribute to distributed team execution: Coordinate effectively with colleagues across locations and time zones, maintaining clear communication and strong collaboration in a remote-first environment.

Requirements

• Extensive machine learning experience: At least 8 years of applied machine learning or artificial intelligence experience, with a demonstrated track record of independently owning technically ambitious systems from design through implementation and production.
• End-to-end engineering ownership: Proven ability to take complex projects from concept to delivery, make sound technical decisions, and drive execution without requiring fully defined requirements or established solutions.
• Comfort with ambiguity: A strong bias for action, a builder mindset, and the ability to work effectively in early-stage or rapidly evolving product environments where experimentation and rapid iteration are essential.
• Python proficiency: Strong programming skills in Python, with experience developing maintainable, efficient, and production-ready machine learning applications.
• Cloud engineering expertise: Hands-on experience building and deploying cloud-based services using AWS, Google Cloud Platform (GCP), or Microsoft Azure.
• Scalable data and inference systems: Experience working with high-volume data, streaming pipelines, real-time inference workloads, and diverse data storage technologies.
• Machine learning fundamentals: Strong understanding of statistical machine learning algorithms, transformer models, large language models, and their practical applications in production environments.
• Research and experimentation: Ability to evaluate emerging ML techniques, formulate testable hypotheses, design meaningful experiments, and translate research findings into reliable software solutions.
• Technical leadership: Demonstrated ability to raise engineering standards through architectural guidance, design reviews, mentorship, and the development of shared technical practices.
• Communication and collaboration: Excellent written and verbal communication skills, with the ability to explain complex technical concepts, align stakeholders, and collaborate effectively across multidisciplinary teams.
• Autonomy and adaptability: Ability to learn new technologies quickly, prioritize effectively, and deliver high-quality results in a fast-paced environment.
• AI agents and conversational systems (preferred): Experience building autonomous agents capable of handling multi-turn conversations while maintaining long-term context, consistency, and relevant customer information.
• Predictive modeling and user behavior (preferred): Strong background in modeling user behavior, designing experiments, and applying causal inference or counterfactual methods when full randomization is not possible.
• Distributed team experience (preferred): Previous experience collaborating with globally distributed engineering teams across different locations and time zones.
• Location and availability: Must be based in India, with eligibility to work in the applicable jurisdiction. Occasional travel may be required for team gatherings, project meetings, or customer engagements.

Benefits

• Remote-first working model: Work remotely from eligible locations in India, including Karnataka, Tamil Nadu, Telangana, Maharashtra, and New Delhi.
• Competitive compensation: Receive a competitive pay package aligned with your skills, experience, and location.
• Generous paid time off: Access time-off benefits designed to support personal commitments and work-life balance.
• Parental leave: Benefit from parental leave provisions to support employees and their families.
• Wellness leave: Access wellness leave to help maintain your physical and mental wellbeing.
• Healthcare benefits: Receive healthcare coverage and other benefits according to the applicable local employment package.
• Retirement savings: Access a retirement savings program, subject to local eligibility and plan terms.
• Technical ownership: Lead ambitious AI/ML projects and influence the design of intelligent customer engagement technologies.
• Continuous learning: Explore emerging AI frameworks, LLM technologies, machine learning techniques, and modern cloud infrastructure.
• Global collaboration: Work with talented professionals in a distributed, international environment that values diverse perspectives and shared learning.
• Career development: Strengthen your technical leadership, research, and production engineering expertise while contributing to high-impact products.
• Community engagement: Participate in supported volunteering and donation initiatives that encourage employees to create positive change in their communities.
• Collaborative culture: Join a team that values innovation, initiative, inclusion, experimentation, and continuous improvement.