Member of Technical Staff (Answer Quality & Evals)
Posted by employer 5 months ago
First seen on Joblaze 5 months ago
Last verified on the company career page 8 hours ago
Skills & Technologies
Requirements
- Experience
- 3+ years
Not disclosed in this posting: compensation, work arrangement, visa sponsorship.
Joblaze summary
In this role, the engineer will focus on developing systems and pipelines that facilitate seamless access to evaluation data for various teams at Perplexity. Key skills include strong Python and SQL proficiency, along with experience in big data systems and AWS environments. This position is ideal for someone with over three years of software engineering experience, particularly those with a background in data engineering and a knack for building robust, scalable solutions. The team operates in a dynamic environment where contributions directly influence the quality of user interactions.
Joblaze insights
- Listed about 5 months ago on Joblaze — tracked directly from Perplexity's career page.
- Perplexity has 116 other open roles including Senior Cinematographer, Brand Studio, Member of Technical Staff (Applied AI Engineer, Agent Capabilities), Strategic Business Operations Lead, API.
- 6546 active Python roles on Joblaze right now.
- 2744 active AWS roles on Joblaze right now.
- 2283 active SQL roles on Joblaze right now.
- Typical Python salary we track runs $125,000–$190,800 (median $160,000, across 3240 roles). See Python salary trends
Quick facts
- How much experience is required?
- At least 3 years of relevant experience for this Member of Technical Staff (Answer Quality & Evals) role.
- What's the tech stack?
- Joblaze extracted these technologies from the posting: AWS, Databricks, Python, SQL, Spark.
- What seniority level is this role?
- Perplexity targets mid-level candidates for this position.
- Is this full-time or contract?
- Full-time for this Member of Technical Staff (Answer Quality & Evals) role at Perplexity.
From the original posting
Perplexity serves tens of millions of users daily with reliable, high-quality answers grounded in an LLM-first search engine and specialized data sources. The Answer Quality team ensures that our prompts, tools, search systems, datasets, and models work together to create the best possible experience for our users.
As our product and agent capabilities evolve, we need evaluation systems that are fast, reliable, production-faithful, and actionable. In this role, you will build and improve the technical foundations that support Answer Quality across Perplexity. This includes our shared evaluation infrastructure and the platform used to replay and analyze agent traces. You will work closely with data scientists, engineers, and product teams to identify quality problems, measure their impact, and turn evaluation findings into product improvements.
Responsibilities
Build shared evaluation infrastructure that helps teams run reliable evals, analyze results, and make product and model decisions
Develop the platform for replaying and analyzing agent traces to reproduce production behavior and diagnose failures
Build and operate scalable systems for processing, storing, and monitoring interaction, trace, and evaluation data
Partner with data scientists, engineers, and product teams to turn answer-quality problems into evaluations, analyses, and product improvements
Operate in a small, high-impact team where your work directly shapes how Perplexity measures and improves Answer Quality
Qualifications
4+ years of software, data, or machine learning engineering experience shipping and operating production systems
Strong proficiency in Python and SQL, with solid fundamentals in system design, data modeling, and distributed systems
Experience building big-data systems, including distributed compute, large-scale storage, and high-volume pipelines
Demonstrated ownership of ambiguous technical projects from initial design through production operation
Ability to work effectively with data scientists, engineers, and product partners
Preferred Qualifications
Experience building evaluation, experimentation, observability, or machine learning infrastructure
Familiarity with LLM and agent systems, including tool use, execution traces, replay, and simulation
Experience building on top of large-scale data processing platforms such as Databricks, Snowflake, or ClickHouse