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About This Role
About the Team \& Role:
We are building the future of customer experience, and our AI Pilots team is at the forefront of this 0\-to\-1 motion. This isn't a role for someone who wants a playbook; this is for the builder who wants to write it. We are looking for a high\-agency individual to join our team as a founding AI Deployment Strategist. Your mission will be to act as a trusted consultant and AI thought leader for our most strategic customers, understand their business from first principles, and deploy cutting\-edge AI agents that deliver undeniable value.
You will operate at the intersection of strategy, sales, consulting, and hands\-on engineering. This role requires the ability to lead executive\-level workshops, design transformative AI solutions, and then roll up your sleeves to bring them to life. If you thrive in ambiguity, take extreme ownership, and are passionate about building what's next, this is the team for you.
What You'll Do:
- Own the AI Lifecycle: Lead end\-to\-end customer engagements, from initial discovery and strategic scoping to hands\-on deployment, value realization, and executive readout.
- Be a Strategic Consultant \& Thought Leader: Act as the primary technical advisor and consultant for our customers. Lead workshops, cut through the industry hype to guide their AI strategy, and build trust\-based relationships with senior stakeholders by connecting technical capabilities to tangible business outcomes.
- Design \& Build AI Agents: Dive deep into customer workflows to design, build, and iterate on AI agents. While you will partner closely with our Forward Deployed Engineering team on complex integrations, you are expected to have the hands\-on ability to configure and deploy agents yourself.
- Develop the Playbook: You are building our deployment motion from the ground up. You will be responsible for creating and refining the playbooks, best practices, and processes that will scale our AI program.
- Drive Business Impact: Identify and prioritize high\-impact use cases that generate clear, decision\-ready signals tied to business value. Your success is measured by the outcomes you deliver for our customers and the growth you drive for Talkdesk.
- Shape the Product: Act as the voice of the customer, providing a tight feedback loop to our Product and Engineering teams to help shape the future of our AI platform.
What We're Looking For:
- 3\+ years of experience in a customer\-facing, technical role such as Solutions Engineering, Technical Consulting, Customer Success, or Product Management.
- Consultative AI Expertise: A consultative approach and the ability to act as an AI thought leader—educating customers on market trends, best practices, and the transformative potential of agentic AI.
- A proven track record of taking ownership over complex, ambiguous projects and delivering measurable results in a fast\-paced environment.
- Strong technical aptitude with hands\-on experience deploying AI, automation, or enterprise SaaS solutions. Direct experience with AI agents, LLMs, and related frameworks is a must.
- Exceptional communication and presentation skills, with the ability to command a room, articulate complex technical concepts to both technical and executive audiences, and drive alignment.
- A "seller mindset" with commercial instincts; you are comfortable engaging in deal conversations and are motivated by driving customer value and expansion.
- High\-agency and a "first principles" approach to problem\-solving. You can see what needs to be done and have the bias for action to do it without being asked.
Bonus Points:
- Experience in a high\-growth startup environment.
- Background in product management, conversation design, or a forward\-deployed engineering role.
- Familiarity with contact center operations or CRM platforms.
Pay Range (OTE Pay): $165,000 \- $207,000
Other Types of Pay: Based on level and role the employee may be eligible for long term incentives in the form of equity and short term incentives of either bonus or commission.
Health Insurance: Medical, Dental, Vision, Life and Disability Insurance, Employee Assistance Program (EAP).
Retirement Benefits: 401(k) plan
Paid Time Off: Talkdesk offers an uncapped paid time off program for exempt employees and an accrual\-based program for non\-exempt employees; both are subject to manager approval and consistent with business needs.
Paid Holidays: Talkdesk offers 14 paid holidays each year.
Paid Sick Leave: Exempt employees have uncapped paid time off and non\-exempt sick leave follows accrual standards; both are subject to manager approval and consistent with business needs.
Method of Application: Apply online.
Application Window: The application window is expected to close at least 5 days from the posting date. The application was posted on 07/09/2026\.
Benefits and perks listed above may vary based on the nature of your employment with Talkdesk.
All questions or concerns about this posting should be directed to the Talent team at [email protected].
Talkdesk is pioneering a new era of Customer Experience Automation (CXA), redefining how the world's most admired brands interact with their customers through AI. Our global team of courageous innovators is customer\-obsessed, building AI\-first solutions that put empathy, trust, and transparency at the center of every interaction. We foster an inclusive culture where diverse perspectives drive our success and every voice belongs. Combining the stability of a global leader with the agility of a disruptor, Talkdeskers are empowered with the autonomy to drive meaningful impact, while giving back to the communities and environment around us.
Talkdesk has been recognized as a Leader in the Gartner® Magic Quadrant™ for Contact Center as a Service (CCaaS) and in the G2 Overall Grid® Reports for AI Agents and Contact Center. With seven consecutive years on the Forbes Cloud 100 and multiple AI Breakthrough awards, there has never been a more exciting time to join us as we shape the future of customer experience automation!
Work Environment and Physical Requirements:
Primarily office\-environment work, extended periods of sitting or standing, computer\-based work. Limited lifting, and equipment usage limited to computer\-related equipment (keyboards, mouse, etc.)
The Talkdesk story hinges on empathy and acceptance. It is the shared goal among all Talkdeskers to empower a new kind of customer hero through our innovative software solution, and we firmly believe that the best path to success for our mission is inclusivity, diversity, and genuine acceptance. To that end, we will hire, promote, work along, cheer for, bond with, and warmly welcome into the Talkdesk family all persons without regard to ethnic and racial identity, indigenous heritage, national origin, religion, gender, gender identity, gender expression, sexual orientation, age, disability, marital status, veteran status, genetic information, or any other legally protected status.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Talkdesk, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Talkdesk AI Hiring
Talkdesk has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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