Develop LLM-powered intelligent experiences that interpret and generate insights from both tabular and unstructured data.
Build and optimize personalized Q&A systems using large language models, enabling context-aware responses tailored to user needs.
Design and enhance conversational talent recommendation systems, combining autonomous agent architectures with personalized recommendation algorithms.
Advance traditional recommendation systems by evolving them from simple ranked lists to multi-topic, interactive experiences that better reflect user intent.
Construct multi-agent intelligent workflows that translate natural language inputs into complex, goal-directed task sequences.
Collaborate within a highly cross-functional team, including data scientists, machine learning engineers, product managers, and UX designers.
Partner with fellow data scientists to design, prototype, and iterate on AI/ML models and system architectures.
Work closely with machine learning engineers to deploy, monitor, and optimize scalable AI/ML solutions in production environments.
Collaborate with product managers to design intuitive user experiences, define feedback loops, and analyze user telemetry to guide product improvements.
Engage in end-to-end AI/ML product development, from ideation to deployment, while continually expanding your technical and product skillset.
Follow and help define robust development standards to ensure the creation of trustworthy, safe, and responsible AI systems.
Contribute to internal and external AI/ML research through experimentation, whitepapers, and collaboration with the broader AI community.

Requirements

Proven experience deploying high-risk NLP applications in real-world, production environments—such as those involving regulatory compliance, privacy, safety, or fairness.
Demonstrated ability to advance and implement Trustworthy AI and Responsible ML practices, working cross-functionally with engineering, legal, policy, and product stakeholders across a large enterprise.
Track record of mentoring and coaching junior data scientists, especially in navigating ambiguous or novel problem spaces.
Strong applied machine learning experience, with solid foundational knowledge in statistics, optimization, and deep learning—preferably gained at leading technology companies (e.g., Google, Meta, Microsoft) or AI-first startups.
Excellent communication skills with the ability to synthesize complex technical work into accessible insights for executive briefings, research publications, and external presentations.
Advanced proficiency in Python and common ML/DS libraries such as NumPy, pandas, scikit-learn, as well as deep learning frameworks like TensorFlow, PyTorch.
Experience designing and deploying scalable deep learning systems, including neural network architecture optimization, model distillation, quantization, or on-device inference.
Strong understanding of machine learning infrastructure, including experience with Kubeflow, MLflow, Airflow is a plus.
12+ years of industry experience, with a demonstrated ability to take ownership of complex projects and deliver impactful AI/ML solutions from concept to production.

Benefits

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Applicant Tracking System Keywords

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Hard Skills & Tools

natural language processingmachine learningdeep learningstatisticsoptimizationneural network architecture optimizationmodel distillationquantizationon-device inferencepersonalized recommendation algorithms

Soft Skills

communicationmentoringcoachingcollaborationproblem-solvingsynthesis of complex informationcross-functional teamworkuser experience designfeedback analysisproduct improvement