Sr. AI Engineer (Full Stack)

5 дн. назад

160.6k–224.9k USD / годСШАSenior

pineconepgvectorsalesforcesapbigquery

Engineering and delivering AI features and tools by building memory architectures, pipelines, and integrations within 's ecosystem.

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We at are looking for a scrappy, creative and fast-moving AI Engineer.
You’ll take raw ideas and rapidly transform them into production-grade applications that empower our global organization. You will architect the Intelligence & Integration Layers that connect autonomous agents directly into ’s enterprise infrastructure.
We don't care about your specific stack or what programming language you grew up with. We care about systems thinking, engineering fundamentals, product sense, and your ability to ship fast. Work at the bleeding edge of the AI ecosystem with top-tier tools and compute resources. What you build goes directly into the hands of real users in days, not months.

5–7+ Years of General Software Engineering. Strong foundational software engineering skills (distributed systems, async patterns, API design, microservices, and databases)
Comfortable pivoting to whatever language or framework gets the job done fastest
Hands-on experience deploying RAG (Retrieval-Augmented Generation) and Agentic Workflows into production using LLM APIs, vector databases, and orchestration frameworks
Proven track record building and maintaining pipelines across fragmented enterprise systems (CRM, ERP, CPQ, marketing platforms) with attention to entity resolution, schema design, and data quality — not just consuming clean data once it exists
Cutting-Edge Stack: Work at the bleeding edge of the AI ecosystem with top-tier tools and compute resources.
Direct Impact: What you build goes directly into the hands of real users in days, not months.
#LI-DD1 #LI-HYBRID

The base salary range represents the low and high end of the anticipated salary range for this position based at our New York City headquarters. The actual base salary offered for this position will depend on numerous factors including, without limitation, experience and business objectives and if the location for the job changes. Our base salary is just one component of ’s competitive total rewards strategy that also includes annual equity awards and an Employee Stock Purchase Plan as well as other region-specific health and welfare benefits.
As an organization, one of our top priorities is to maintain the health and wellbeing for our employees and their family. To achieve this goal, we offer robust and comprehensive benefits including:
Medical, dental and vision insurance
Generous paid time off policy
Short-term and long-term disability
Access to mental health services
401k, tuition reimbursement and student loan paydown plans
Employee Stock Purchase Plan
Fertility and adoption support and up to 18 weeks of paid parental leave
Child care and family care discounts
Free access to Digital App and apparel and product discounts
Commuter benefits and Citi Bike Discount
Pet insurance and so much more!
Base Salary Range
$160,600 — $224,950 USD

Take ambiguous problems and turn them into functional, high-performing AI features and standalone tools
Solve the "State Problem" for AI by building memory architectures and vector stores (Pinecone, Pgvector) that maintain context while strictly adhering to data retention and deletion policies
Build pipelines and entity-resolution logic that connect data across CRM, ERP, CPQ, and marketing systems (e.g. Salesforce, SAP, BigQuery) into a single, reliable source of truth agents can act on
Embed with business units to understand their workflows, identify high-value opportunities, and select the right initial use cases to prove value
Embed closely with stakeholder teams, understand their needs, and guide adoption of what you build
Share field feedback that helps other teams understand where the models succeed and where they can improve
Deploy continuous evaluation pipelines to test models for hallucinations, prompt injections, data leakage, and performance regressions before code hits production
Implement Semantic Caching and smart routing logic (directing routine queries to smaller, cheaper models) to maintain low latency and efficient inference costs