Principal Engineer - Data Science

Overview

Job Title:

Principal Engineer:
Data Science.

Type:
Full time Role.

Location:

Sunnyvale, CA.

Hybrid Role.

1 prescreening Round, Then three client virtual rounds.

Candidates must be authorized to work in the U.S. without current or future sponsorship requirements.

• GEN AI.
• Prompting and Fine Tuning.
• LLM, RAG, BERT, GPT.
• Principal Engineer should have Lead experience.

• 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.

• Proven experience deploying high-risk NLP applications in real-world, production environments—such as those involving regulatory compliance, privacy, safety, or
• 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 Num Py, pandas, scikit-learn, as well as deep learning frameworks like Tensor Flow, 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.

• Hands-on experience with Text-to-SQL or Text-to-Cypher based application, or the design of modern recommender systems.
• Prompt engineering, retrieval-augmented generation (RAG), or open-weight model customization.
• Publication history in top-tier ML/NLP conferences such as NeurIPS, ICML, ACL, EMNLP, or ICLR.

Regards,

Purnima Pobbathy

Senior Technical Recruiter

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