Sr. Applied AI Engineer

Organizations rely on Redis when every millisecond counts—whether it’s powering game leaderboards, fraud detection, or conversational AI. As generative AI moves from POC to production, Redis’ native search, semantic caching, and flexible data structures provide agent frameworks and RAG pipelines with a fast, dependable memory layer. We’re doubling down on the AI ecosystem and scaling the team, creating new opportunities to shape the next chapter of real-time, intelligent apps.

You’ll join an influential, high-autonomy engineering team whose work powers Redis’ most visible AI integrations. One sprint you may publish a new LangGraph connector; the next you’ll pair with a Fortune 100 customer to harden a pilot RAG service. Your work ships in the open (on GitHub), in conference workshops, and inside customer proofs of concept—so you’ll drive immediate company revenue while also building a strong public portfolio. Come join an incredible team, ecosystem, and company working on the product that millions of developers love.

Develop/maintain projects (Python) using JS, C/C++, Go, Java; provide consultation during sales calls, leverage expertise in AI; participate in sales meetings, enablement, and other events as  subject matter expert for Redis Vector Similarity Search; design/develop deep learning algorithms, machine learning recommendation systems; perform data analyses/visualization (SQL, Pandas, Numpy, Scikit-Learn, MatplotLib); deploy machine learning algorithms (AWS), graph clustering algorithms (Python, C++, Rust) using programming languages; use software libraries/platforms, AWS Sagemaker, Numpy, Scipy, Scikit-Learn, PyTorch, Snowflake, AWS Neptune); develop cutting-edge features; address issues; write software using impactful frameworks in AI; work with clients to create customized Redis AI-powered solutions for specific business/data service needs; provide technical consultation during sales calls for Redis Vector Similarity; perform data analysis/research on internal/external datasets using notebooks (Jupyter); participate in peer code reviews (GitHub) to ensure team standards; reduce bugs/quality issues; write/maintain automated software tests (PyTest) to ensure accurate, high quality code; research/implement fixes to client reported issues in Redis AI libraries; communicate status (GitHub, JIRA, and Zendesk); create/maintain self-contained examples to demonstrate use of Redis AI libraries (Jupyter, Docker); communicate status/proactively address issues; attend daily progress/cross department meetings; create/review Redis promotional materials for public; create documentation for AI software projects using internal/external CMS (Markdown READMEs); review academic research on AI using resources (ARVIX, Google Scholar) to adapt/apply new ideas to solve client problems; and support client technical deep dive session (AI, Redis libraries). This is a telecommuting position with remote benefit from various unanticipated locations throughout the U.S.

Job Requirements: 

Master’s degree or foreign equivalent in Computer Science, Computer Engineering, or Mechanical Engineering plus 1 year experience. Experience to include designing/developing deep learning algorithms, machine learning recommendation systems; performing data analyses/visualization (SQL, Pandas, Numpy, Scikit-Learn, MatplotLib); deploying machine learning algorithms (AWS), graph clustering algorithms (Python, C++, Rust) using programming languages; and using software libraries/platforms, AWS Sagemaker, Numpy, Scipy, Scikit-Learn, PyTorch, Snowflake, AWS Neptune). This is a telecommuting position with remote benefit from various unanticipated locations throughout the U.S.

The estimated gross base annual salary range for this role is $220,000 - 238,000  per year in New York, California, Washington, Colorado, and Rhode Island. Actual compensation may vary and is dependent on various factors, including a candidate’s work location, qualifications, experience, and competencies. Base annual salary is one component of Redis’ total compensation and competitive benefits package, which may include 401(k), unlimited time off, learning and development opportunities, and comprehensive health and wellness benefits. This role may include discretionary bonuses, stock options, commuter benefits based on location, or a commission plan. Salary history is not used in compensation package decisions. Redis utilizes market pay data to determine compensation, so posted compensation ranges are subject to change as new market data becomes available.

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