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About This Role
Role Details
Location: Preferred San Francisco\- hybrid schedule, will consider remote
About Us
Crescendo is the first AI‑native contact center — combining auto‑tuning AI with multilingual human experts to deliver performance‑guaranteed customer experiences. In a category full of hype, we stand apart by delivering measurable outcomes at speed and scale.
We don’t just ship software. We deliver results — guaranteed.
The Role
We’re hiring multiple AI Innovation Leads to help embed practical, process‑level AI across Crescendo. These roles are hands‑on builders and operators who turn AI capability into real business impact inside functions like GTM, Operations, G\&A, Product, and Corporate teams.
This role may sit in different parts of the organization depending on deployment and can be hired as either full‑time or contract.
You’ll design and deploy mini‑assistants, copilots, reusable AI personas, workflow automations, and lightweight agents (e.g., Gemini Gems or equivalent) that integrate directly into how teams work every day.
Your mandate: move teams from experimenting with AI to operating with AI.
What You’ll Do:
Build Process\-Embedded AI
- Design and deploy mini‑assistants, copilots, reusable AI personas, and workflow automations
- Build lightweight agents (e.g., Gemini Gems or equivalent) embedded into team workflows
- Translate manual or fragmented work into AI‑enabled execution
- Ensure solutions are reusable, scalable, and measurable
### Identify High‑Leverage Automation Opportunities
- Evaluate workflows for automation potential
- Reduce manual effort across planning, reporting, coordination, and execution
- Improve speed, consistency, and quality through AI‑driven workflows
- Help teams move from prompts to productionized usage
### Translate AI Insight Into Execution
- Convert AI outputs into structured, trackable workstreams
- Break initiatives into clear deliverables with owners and timelines
- Support adoption inside tools like Asana, Jira, CRM platforms, or operational systems
### Strengthen the Company’s AI Operating Muscle
- Integrate AI into team planning and execution
- Increase visibility into cross\-functional AI opportunities
- Build and scale repeatable AI workflows and standards
- Teach and train teams on the AI workflows and improvements you create to drive adoption
- Enable others to independently use AI effectively
- Help teams integrate AI into planning and execution rhythms
What We're Looking For:
- 2–6 years of experience in consulting, business operations, product ops, GTM ops, automation, or similar roles
- Hands‑on experience applying AI to real workflows (not just experimentation)
- Experience building assistants, copilots, reusable prompt systems, or lightweight agents
- Strong ability to translate ambiguous opportunities into shipped solutions
- Comfort working across functions and navigating fast‑moving environments
- Ownership mindset with bias toward action
Strong Signals (Nice to Have)
---------------------------------
- Experience building Gemini Gems or similar agent workflows
- Experience inside high‑growth or AI‑native companies
- Familiarity with SaaS GTM or contact center operations
- Experience evaluating AI vendors and tooling ecosystems
- Experience supporting transformation initiatives inside teams
- MBA or strong business background (e.g., strategy, operations, consulting) is a plus
- Ability to work a weekly hybrid schedule in our San Francisco office
What Success Looks Like:
- Teams adopt assistants that meaningfully reduce manual work
- AI workflows move from experimentation to daily usage
- High‑impact automation opportunities are identified early and shipped quickly
- Functional teams operate faster with clearer execution leverage
- Crescendo builds durable, reusable internal AI capabilities
Why Crescendo?
- Performance\-Guaranteed CX The only model with skin in the game.
- Category Leadership We’re not joining the conversation. We’re writing it.
- Velocity \& Scale From mid\-market agility to enterprise impact, Crescendo moves fast and delivers faster.
- AI \+ Superhumans Not one or the other — the best of both, working together.
Compensation Includes:
Compensation varies based on experience, location and engagement type (full‑time vs contract) and may include:
- Base Salary: $150,000 \- $170,000 (full‑time roles)
- Performance bonus eligibility (full‑time roles)
- Equity participation (full‑time roles)
Join us to define the future of customer experience in the AI era.
Company Culture Is At Our Core
Core values give our work intention and our culture its edge. They’re the standards we hold for ourselves, our partners, and each other.
- Care for others: Empathy is a key driver. When people thrive, so does the mission.
- Embrace growth: Curiosity fuels progress. Take bold risks, sharpen your edge, go forward.
- Manifest trust: Trust is our currency. Earn it daily, protect it fiercely, and let it fuel what’s next.
- Take ownership: Bold choices with integrity at the core—that’s how impact lasts.
- Be humble: Humility opens the door to better ideas. Hear others, lift others, keep learning.
Crescendo is proud to be an equal\-opportunity workplace. We value diversity, inclusion, equity, and belonging and these pillars are at the heart of how we work together. We are committed to providing equal employment opportunities regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, Veteran status, or any other applicable legally protected characteristics in the location in which the candidate is applying. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements.
We are committed to the inclusion of all individuals and will make reasonable accommodations for qualified individuals with disabilities in our job application process. If you require assistance or accommodations to participate in the job application or interview process, please contact [email protected].
PRIVACY NOTICE
Crescendo is committed to ensuring your privacy and the protection of your personal data. By filling out the forms associated with your job application and submitting your data to us, you are giving us your consent to process your data and store it for potential recruitment and hiring purposes.
To understand more about Crescendo’s privacy program, including your rights and options for managing the personal data you submit to us, please visit our Privacy Center here.
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Salary Context
This $150K-$170K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Crescendo.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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($160K) sits 12% below the category median. Disclosed range: $150K to $170K.
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.
Crescendo.ai AI Hiring
Crescendo.ai has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span San Francisco, CA, US, Remote, US. Compensation range: $170K - $250K.
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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