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About This Role
Location : San Francisco, United States (Hybrid \- 3 Days In Office)
About Us
At Relevance AI, we’re building the home of the AI workforce.
Our mission is simple: empower every team to delegate meaningful work to AI agents that think, act, and collaborate like experts.
With Relevance AI, anyone can create and manage intelligent agents that handle workflows, decisions, and collaboration \- all within one unified platform. Our technology already powers industry leaders such as Canva, Databricks, Confluent, Autodesk, Lightspeed, Rakuten, Aveva, Qualified, and Activision Blizzard, helping them scale excellence across operations, marketing, and sales.
We’re backed by Bessemer Venture Partners, Insight Partners, Peak XV, and King River Capital, and raised our Series B in April 2025 to accelerate growth and push the boundaries of agentic automation.
Headquartered in San Francisco and Sydney, we operate on a hybrid model and thrive on curiosity, collaboration, and execution \- we move fast, think big, and win together.
This year, we were proud to be named LinkedIn’s \#1 Startup in Australia.
If you want to define how the world works with AI, join us.
The Role
We’re looking for an AI Solution Consultant to join our dynamic team with a primary focus on post‑sales implementation. We are open to varied experience levels across post‑sales; this role blends hands‑on delivery with light pre‑sales support. As an AI Agent Solutions Consultant at Relevance AI, you’ll help Enterprise customers leverage AI agents to solve core business problems.
Using our low‑code/no‑code platform (and code where it counts), you will co‑design, architect, and develop production‑grade AI agents at Enterprise scale. You will also be an evangelist and enabler of our technology \- driving technical success, product adoption, and measurable outcomes during high\-impact implementations with our most important customers.
This is an exciting opportunity for someone passionate about agentic AI, creative problem‑solving, and working directly with customers to transform their businesses with intelligent automation.
*We practice always‑on hiring as we scale the Solutions Consulting team and regularly add exceptional talent to our growing group.*
Your impact
- Lead post‑sales implementations end‑to‑end (discovery design build UAT launch hypercare), owning scope, delivery, customer enablement, and business impact.
- Build and integrate production AI agents with customer systems via REST/GraphQL, webhooks, and events; handle auth (OAuth2/JWT), data mapping, and robust error handling.
- Configure agent workflows, prompts, tools, and retrieval/RAG; establish evaluation, guardrails, and reliability standards for quality and safety.
- Develop lightweight Python automations and custom connectors/middleware to meet integration needs.
- Set up observability (logging, metrics, tracing, alerting) and create clear runbooks, playbooks, and technical documentation.
- Provide Tier 2/3 troubleshooting and root‑cause analysis; drive durable fixes and continuous improvement post‑go‑live.
- Enable and train customers to be self‑sufficient builders on the Relevance AI platform through workshops, onboarding, co\-development sessions and best practices sessions.
- Act as a strategic partner by channeling customer insights to Product; influence roadmap and reusable solution patterns.
- Identify expansion opportunities with Customer Success based on delivered value and measurable outcomes.
- Support implementation efforts pre‑sales (\~20%) with targeted discovery, use case selection and implementation planning.
What We’re Looking For
- 4\+ years in Solutions/Professional/Implementation (Enterprise preferred).
- Strong Python and API integration skills (REST/GraphQL, OAuth2, webhooks, OpenAPI; Postman/Insomnia).
- Hands‑on with LLMs: prompt design, RAG, embeddings, vector DBs, orchestration (e.g., LangChain/LlamaIndex), and major model providers.
- Able to translate business needs into pragmatic technical solutions; excellent customer communication.
- Experience building productionizing use cases on low‑code SaaS and/or AI platforms.
- Cloud and delivery fundamentals (AWS/GCP/Azure, Git, Docker, CI/CD, secrets); familiarity with security/privacy (e.g., SOC 2, data residency).
- Customer‑first mindset, bias to action, and continuous learning.
- Nice to have: RFP/security questionnaire responses, solution diagrams, SOW/LOE contributions.
- Occasional travel to customer sites might be required.
Why Join Us?
- Work at the forefront of AI with a nimble team that is constantly pushing boundaries. We encourage and celebrate ideas that drive our mission forward.
- We're guided by our five values: truth\-seeking, being empathetic, putting the customer first, iterate extremely fast, and building memories.
- We’ve set high standards in our high\-trust environment—we hire exceptional people to do great work. In return, we reward our people with competitive salaries, unparalleled professional growth and career\-defining opportunities.
- Relevance AI is well\-funded by leading investors, including Insights Partners, Peak XV, King River Capital.
- As an early team member, you’ll play a key role in shaping our future—including our culture, ways of working, and even the benefits we offer. We’re laying the foundations now, and your ideas can help define what comes next.
Benefits
- Health Insurance Contribution – Relevance AI contributes to the cost of individual medical, dental, and vision insurance for employees.
- Commuter Benefits – Save on your commute with pre\-tax deductions for transit and parking expenses
- Unlimited Annual Leave – Flexible time off policy to rest, recharge, and take care of what matters most
- ESOP – Employee Stock Ownership Plan so you can grow with the company
- AI Productivity Benefit – Get up to $1200 USD/year to spend on AI tools, courses, and learning resources that help you work smarter and grow your skills
- Parental Leave – We offer 12 weeks of paid parental leave for all eligible new parents, and an additional 6 weeks for the birthing parent
- Milestone Merch – Celebrate your work anniversaries with customised Relevance AI swag
- Food, Drinks \& Community – Stay energised with free breakfasts, healthy snacks, and a fully stocked fridge of drinks. Enjoy team lunches provided every Tuesday, Thursday and Friday, plus Uber Eats dinners and regular catered office meals throughout the week. As the home of the AI workforce, we also host vibrant community events featuring thought leaders, industry partners, and the wider tech community.
- Quarterly Team Events – Build stronger connections through fun, meaningful team bonding experiences every quarter
- Social Clubs – Share your hobbies and interests by joining or starting a club with your teammates. From hiking and chess to board game nights and social committee activities—there’s something for everyone!
- Sonder EAP – Access 24/7 mental health and wellbeing support through Sonder, our Employee Assistance Program
Work Authorization: At this time, we are not able to sponsor visas or transfer immigration status for candidates applying to U.S.\-based roles. You must be authorized to work in the United States without current or future sponsorship.
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Relevance AI, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Relevance AI AI Hiring
Relevance AI has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US.
Location Context
AI roles in San Francisco pay a median of $253,000 across 2,168 tracked positions. That's 26% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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