Interested in this AI/ML Engineer role at Crescendo.ai?
Apply Now →Skills & Technologies
About This Role
Role Details
Location: Flexible/Remote
About Us
Crescendo represents peak CX performance in the AI era. We combine world\-class outsourcing expertise with innovative technology to set a new standard in CX and operations, delivering results that scale and support that never sleeps.
More than that, Crescendo is about people. We don’t just connect talent with opportunity—we create a place where careers grow, ideas thrive, and people are empowered to make an impact. Join us at Crescendo, and let’s build the future of customer experience together.
Welcome to Crescendo. Welcome to what’s next.
The Role
Crescendo is an AI\-native contact center platform and CX operator for hundreds of enterprise clients. As part of this product offer, Crescendo is researching agentic approaches to workforce management. Specifically, the most advanced LLM AI technology to consider operational data and produce CX forecasts, staffing plans, CSAT predictions, and labor cost projections.
We’re looking for a high agency technical Product Manager with Workforce Management (WFM) experience who is comfortable collaborating with operational leaders and also uses agentic coding tools to build software. While supported by an elite agentic engineering team for scalable and secure product delivery, this product manager would be building a fully working first pass of this system.
This product will include agents to gather and organize operational data from multiple sources including the Crescendo technology platform as well as other IT systems such as time clock, HR, and finance software. This data will be consumed by a newly created WFM service that will produce forecasts and agent schedules for all Crescendo people operations. This service will also be available to enterprise customers to employ their own CX staff and use Crescendo as a technology\-only service.
As a Crescendo Product Manager you would work closely with engineering and interface directly with operations leadership for Crescendo as well as various functions including IT.
If you’ve worked with WFM systems and want to help reinvent this software with an AI\-native delivery model, this role is for you.
What You’ll Do:
Forecasting automation
- Build automated volume forecasting from Crescendo operational datasets
- Translate forecasts into staffing and headcount requirements
- Improve forecast accuracy through agentic self\-improvement
Multitenant workforce planning
- Design staffing logic supporting shared associates across partner programs
- Model cross\-program capacity allocation
- Enable dynamic queue balancing across workloads
Agentic planning workflows
- Define how forecasts drive operational decisions inside Crescendo
- Build AI\-first workforce planning workflows
- Move workforce planning from manual scheduling toward automation
Systems integration
- Connect forecasting to full Crescendo IT stack: CRM/CC, HR, Finance.
- Connect data collection agents to every major CCaaS and ticketing platform.
- Build a closed feedback loop on forecast accuracy and schedule adherence.
Hands\-on product building
This role includes implementation—not just specification.
You will:
- Prototype workflows using AI developer tools
- Build lightweight services, scripts, and assistants (week, not months)
- Experiment with frontier models to evaluate best LLM model and agentic loop to perform WFM
Direct Workforce Management experience is required
Examples:
- Supporting capacity planning in CC / BPO environments
- Working with Erlang\-based staffing logic
- Designing staffing models across queues or programs
- Supporting intraday planning or long\-range forecasting workflows
- Implementing or integrating WFM platforms
Experience with systems such as:
- NICE
- Verint
- Genesys
- Calabrio
- Aspect
- or equivalent WFM environments
Technical Capabilities We’re Looking For
You don’t need to be a full\-time engineer—but you do need to be a builder and LLM AI enthusiast as this technology drives our product strategy.
Examples:
- Writing code using AI developer tools (Codex or Claude)
- Working directly with structured datasets
- Prototyping forecasting workflows
- Translating operational logic into executable automation
- Partnering closely with data science teams
Strong candidates often have backgrounds in:
- Technical product management
- product engineering
- applied analytics platforms
- AI\-enabled operations tooling
What Success Looks Like in the First 3 Months
- Forecasts automatically generate staffing recommendations across programs
- Multitenant staffing scenarios modeled inside Crescendo
- Labor cost visibility embedded into planning workflows
- Operations teams rely on forecasts to plan delivery
- Forecasting outputs integrate cleanly with HR and finance systems
Ideal Backgrounds for This Role
This role is especially strong for candidates who have:
- Built forecasting or staffing tools inside WFM platforms
- Supported CX or BPO delivery environments
- Owned capacity planning infrastructure
- Worked on workforce analytics platforms
- Implemented WFM integrations
- Built internal automation around staffing workflows
Bonus if you’ve worked on:
- multitenant staffing models
- agent\-based workflows
- planning automation assistants
- AI\-enabled scheduling or forecasting tools
Here’s What’s On the Table:
- Take on challenges that actually move the needle in an industry ready for change.
- Earn competitive pay while building a career with endless opportunities.
- Enjoy remote work with the focus and flexibility to do your best work.
- Grow in an environment that rewards ambition and sharp execution.
- Thrive in a team environment where collaboration is the foundation of your success.
- Be part of a people\-first, values\-driven organization
- Work with innovative global partners and diverse teams
Compensation Includes:
Compensation varies based on experience, location and engagement type (full‑time vs contract) and may include:
- Base Salary: $150,000 \- $250,000 annually
- Performance bonus eligibility
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.
rmYso9oire
Salary Context
This $150K-$250K range is above 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. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($200K) sits 10% above the category median. Disclosed range: $150K to $250K.
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.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.