Interested in this AI/ML Engineer role at PineCone?
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About This Role
About Pinecone
Pinecone is the trusted AI knowledge company. Its trusted AI knowledge platform—including its Database, Nexus, and Marketplace products—power accurate, fast, and cost-effective AI applications for more than 10,000 customers and 1M developers worldwide. Pinecone's mission is to make AI knowledgeable.
About the Team and Role
We're looking for an Applied AI Engineer who loves building real applications with AI. You'll work across the stack, from backend services and retrieval pipelines to APIs and user-facing applications, turning new AI capabilities into working solutions.
This is a hands-on engineering role for someone who enjoys moving quickly, experimenting with emerging technologies, and owning projects from initial idea to production.
You'll work closely with our engineering and product teams, as well as customers and partners, to explore new use cases, build reference applications, and demonstrate what's possible with Pinecone's technology.
The ideal candidate combines strong full-stack engineering skills with practical experience building LLM-powered applications and agentic systems. You should be equally comfortable discussing architecture, writing production code, and putting together a working prototype.
Responsibilities
- Design, build, and ship end-to-end AI applications using Pinecone’s retrieval and knowledge infrastructure.
- Develop full-stack solutions spanning frontend interfaces, backend services, APIs, data pipelines, and LLM integration.
- Build reference applications, integrations, and reusable components that help developers and customers adopt Pinecone.
- Experiment with emerging AI patterns, including RAG, agents, tool use, context engineering, and agent memory.
- Work with customers, partners, and internal teams to understand real-world problems and translate them into working software.
- Evaluate and improve the quality, reliability, latency, and cost of AI-powered applications.
- Share technical insights and best practices through code, examples, and technical content.
- Bring learnings from building applications back into Pinecone’s product and engineering teams.
What You’ll Bring to the Table
You don't need to check every box, but you should enjoy building software and be excited about what AI makes possible.
Full-Stack Engineering
- Strong software engineering background with experience designing, building, and shipping production applications.
- Proficiency in Python and/or TypeScript, including modern backend frameworks and frontend technologies such as React or Next.js.
- Experience building APIs, integrating external services, and working with databases and cloud infrastructure.
- Ability to independently take a project from rough requirements through implementation and deployment.
Applied AI
- Hands-on experience building applications using LLMs, embeddings, vector databases, or retrieval-augmented generation.
- Familiarity with modern AI development tools, frameworks, and APIs.
- Understanding of the practical challenges of building AI applications, including retrieval quality, evaluation, latency, reliability, and cost.
- Curiosity about emerging patterns in agentic applications, context management, and AI-driven workflows.
Ownership & Collaboration
- A builder's mindset: you enjoy writing code, experimenting, and getting working software into users’ hands.
- Comfortable working with ambiguous requirements and making pragmatic technical decisions.
- Ability to communicate technical ideas clearly to engineers, customers, and other stakeholders.
- Comfortable moving between rapid prototypes and production-quality implementations.
- Interest in understanding how developers and customers use the technology and translating that feedback into better solutions.
Bonus Points
- Experience with Pinecone or other vector databases and search technologies.
- Experience building agentic applications, agent memory systems, or complex RAG pipelines.
- Contributions to open-source projects, developer tools, or technical communities.
- Experience working directly with customers on technical projects.
- Experience deploying and operating AI applications in production.
Perks & Benefits
- Comprehensive health coverage including medical, dental, vision, and mental health resources
- 401(k) Plan
- Equity award
- Flexible time off
- Paid parental leave
- Annual Company Retreat
- WFH Equipment Stipend
Benefits and eligibility may vary by country of employment.
Compensation Range: $190K - $270K
Salary Context
This $190K-$270K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 2239 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At PineCone, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($230K) sits 8% above the category median. Disclosed range: $190K to $270K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
PineCone AI Hiring
PineCone has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $270K - $270K.
Location Context
AI roles in New York pay a median of $221,000 across 3,257 tracked positions. That's 4% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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