Member of Technical Staff (Machine Learning Engineer, Ranking Quality - Search)
Join Perplexity as a Machine Learning Engineer to enhance search quality through innovative ranking solutions.
Skills & Technologies
Requirements
- Experience
- 5+ years
Joblaze summary
In this role, the Machine Learning Engineer focuses on enhancing search quality by tackling complex ranking challenges from start to finish. The position requires expertise in neural ranking methods or production ranking systems, along with strong skills in machine learning and software engineering. Ideal candidates will have at least five years of experience and a proven track record in managing large-scale ranking systems. The role involves collaboration across various teams, emphasizing ownership of quality outcomes in a dynamic environment.
Joblaze insights
- Listed 11 days ago on Joblaze — tracked directly from Perplexity's career page.
- Perplexity has 101 other open roles including Litigation Paralegal, Principal Counsel, Corporate, Sr. Executive Sourcer.
- 253 active Machine Learning roles on Joblaze right now.
- 82 active LLM roles on Joblaze right now.
- 3 active Neural Networks roles on Joblaze right now.
- Typical Machine Learning salary we track runs $151,000–$253,300 (median $192,000, across 169 roles). See Machine Learning salary trends
Quick facts
- How much experience is required?
- At least 5 years of relevant experience for this Member of Technical Staff (Machine Learning Engineer, Ranking Quality - Search) role.
- What's the tech stack?
- Joblaze extracted these technologies from the posting: LLM, Machine Learning, Neural Networks, Ranking Systems.
- What seniority level is this role?
- Perplexity targets senior candidates for this position.
- Is this full-time or contract?
- Full-time for this Member of Technical Staff (Machine Learning Engineer, Ranking Quality - Search) role at Perplexity.
From the original posting
Perplexity is seeking an experienced Machine Learning Engineer to improve search quality across the middle and later stages of ranking. We are looking for a strong ranking generalist who can own ambiguous problems end to end and brings exceptional depth in either neural ranking or production ranking systems.
Responsibilities
Relentlessly push search quality forward through models, data, evaluation, infrastructure, or any other leverage available.
Own ranking-quality problems end to end: define the evaluation, identify the bottleneck, build the solution, and ship it safely.
Train and evaluate retrieval, ranking, and classification models, including neural and LLM-based approaches where appropriate.
Build and operate ranking infrastructure, including feature computation, low-latency inference, multi-stage cascades, deployment, and monitoring.
Make sound trade-offs across quality, latency, reliability, cost, and engineering complexity.
Collaborate across Data, AI, Infrastructure, and Product while retaining ownership of the final quality outcome.
Qualifications
Deep understanding of search or recommender systems and their evaluation.
Proven ownership of a large-scale production ranking system or a substantial class of quality problems.
Strong machine-learning and software-engineering skills across data, models, serving, and monitoring.
Ability to drive ambiguous, cross-team problems without continuous task decomposition.
Exceptional depth in either modern neural ranking methods or low-latency ranking systems and runtime.
Minimum 5 years of relevant industry experience.