Member of Technical Staff (Software Engineer, Applied AI)

Kindredventures; Palo Alto (CA); On-site; USD 120,000 - 160,000; Full time

Job summary

Pantera Capital is seeking an Applied ML Engineer to design, build, and iterate on advanced AI models that drive personalization and content discovery. You will leverage your extensive machine learning experience to solve key challenges and collaborate with cross-functional teams. Ideal candidates have 5+ years of impactful ML/AI model development, strong software engineering skills, and a deep understanding of emerging technologies. This role is hybrid based in Palo Alto, California, and offers an innovative work environment.

Qualifications

• 5+ years experience building and shipping robust ML/AI models for large-scale, user-facing or data-driven products.
• Deep expertise in deep learning, LLMs, information retrieval, content summarization, recommendation systems, and ranking.
• Strong software engineering skills and collaborative development experience.

Responsibilities

• Apply ML and LLM techniques for personalization, query understanding, and content discovery.
• Evaluate models with both offline and online techniques, designing experiments.
• Own the model lifecycle from research to production, including A/B testing.

Skills

Machine Learning

Artificial Intelligence

Deep Learning

Python

NLP

Software Engineering

Education

BS, MS, or PhD in Computer Science, Engineering, or related field

Tools

PyTorch

TensorFlow

JAX

Job description

Location

San Francisco

Employment Type

Full time

Location Type

Hybrid

Department

AI

Perplexity is looking for an Applied ML Engineer to design, build, and iterate on cutting-edge AI models powering our core experience. As an expert in machine learning and artificial intelligence, you will develop scalable and impactful solutions for user personalization, query understanding, and content discovery - fulfilling the curiosity of millions of users across the globe.

Key Responsibilities

• Apply state-of-the-art ML and LLM techniques to solve problems spanning:

  • Personalization (LLM memory, context summarization, retrieval and ranking);

  • Query Understanding (intent modeling, rewriting, agentic decomposition);

  • Content Discovery (feed ranking and surfacing)

• Rigorously evaluate LLM/ML models with both offline and online techniques, designing experiments and metrics that provide deep insight into quality and impact.

• Own the entire model lifecycle from research to production: data analysis, modeling, evaluation, offline/online A/B testing, and iterative improvement.

• Collaborate cross-functionally with engineers, PMs, data scientists, and designers to ensure our AI drives meaningful product improvements.

• Stay at the forefront of ML/AI innovation by evaluating and incorporating emerging research and algorithms into the product lifecycle.

Preferred Qualifications

• 5+ years experience building and shipping robust ML/AI models for large-scale, user-facing or data-driven products.

• Deep expertise in deep learning (PyTorch, TensorFlow, JAX), LLMs, information retrieval, content summarization, recommendation systems, NLP, and/or ranking.

• Strong software engineering skills (Python, production-quality codebases, collaborative development).

• In-depth experience with the full ML lifecycle: data analysis, feature engineering, iterative model development, rigorous evaluation, and ongoing monitoring/improvement.

• Proven collaborator and communicator; excels in high-velocity, cross-functional teams.

• Curious, driven by end-user/product impact, and passionate about advancing the state of applied ML and AI.

• BS, MS, or PhD in Computer Science, Engineering, or related field (or equivalent experience).

Bonus Points For

• Experience with LLM prompt engineering, Retrieval-augmented generation (RAG) based systems.

• Experience in large scale user-centric and content-centric personalization challenges (user modeling, retrieval, content ranking, etc).

• Open-source or published contributions in ML, NLP, IR, or relevant research fields.