AI Engineer, Applied ML
USD 200,000-300,000 per year
Used Tools & Technologies
Not specified
Required Skills & Competences
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Python @ 6
A/B Testing @ 3
Algorithms @ 3
TensorFlow @ 3
Data Analysis @ 3
NLP @ 3
LLM @ 3
PyTorch @ 3
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
Perplexity is looking for an Applied ML Engineer to design, build, and iterate on cutting-edge AI models powering the core experience. You will develop scalable and impactful solutions for user personalization, query understanding, and content discovery used by millions of users.
Responsibilities
- Apply state-of-the-art ML and LLM techniques to problems including:
- 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; design experiments and metrics to measure 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 AI drives meaningful product improvements.
- Stay current with ML/AI research and incorporate emerging algorithms into the product lifecycle.
Requirements
- 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; able to excel in high-velocity, cross-functional teams.
- BS, MS, or PhD in Computer Science, Engineering, or related field (or equivalent experience).
Bonus Points
- Experience with LLM prompt engineering and Retrieval-Augmented Generation (RAG) systems.
- Experience with large-scale user-centric and content-centric personalization challenges (user modeling, retrieval, content ranking).
- Open-source or published contributions in ML, NLP, IR, or related research fields.
Compensation & Location
- Base salary range: $200K–$300K. Offers equity.
- Workplace: Hybrid (San Francisco). Additional locations: New York City; Palo Alto.