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About This Role
Company Description
Organizations everywhere struggle under the crushing costs and complexities of “solutions” that promise to simplify their lives. To create a better experience for their customers and employees. To help them grow. Software is a choice that can make or break a business. Create better or worse experiences. Propel or throttle growth. Business software has become a blocker instead of ways to get work done.
There’s another option. Freshworks. With a fresh vision for how the world works.
Freshworks Inc. builds uncomplicated service software that delivers exceptional employee and customer experiences. Our people\-first approach to AI eliminates friction, helping businesses reduce complexity, lower cost\-to\-serve, and deliver faster, more human support through enterprise\-grade yet easy\-to\-use CX and IT solutions. Nearly 75,000 companies, including Bridgestone, New Balance, Nucor, S\&P Global, and Sony Music, trust Freshworks to power their Employee Experience (EX) and Customer Experience (CX) operations.
Fresh vision. Real impact. Come build it with us.
Job Description
Freshworks is looking for a highly motivated and experienced Director of Product Management for our AI team. This role will be pivotal in driving our AI Agent product line forward focusing on employee experience including IT Service Management, HR Service Management, and IT Operations Management. The role requires a good understanding of AI Agents and IT Service Management \& IT Operations Management use cases. This role will closely collaborate with product, engineering and GTM leadership to drive decisions and initiatives to build a compelling value proposition and drive adoption \& usage with customers.
Responsibilities:
- Define the long\-term strategy and vision for the product, leveraging broad customer research, customer experience and new technologies to deliver features to our customers
- Bring value addition by thinking big and continuously pitching new product ideas
- Drive requirement definition, customer experience design, product roadmap and prioritization
- Define and own the business metrics and OKRs for our products
- Ability to translate complex processes into technical requirements while also managing delivery and operations
- Work with the Product Leadership to prioritize problems or themes to reach the outcomes
- Define the outcome for sprints and releases and prioritizes the initiatives for the scrum teams
- Work with the developers and designers to build a product that meets the expected outcomes and market needs
- Work with both UX and Development to understand design or technology implications in solution ideas
Qualifications
- 15\+ years of working in global/cross\-functional software teams in a software product company. SaaS experience is preferred as you’d settle right in.
- 10\+ years of core product management experience in building features for internet software products. Past experience managing employee experience products such as ITSM, HCM, HRMS is desirable.
- 3\+ years experience of managing AI / ML / NLP / Agentic AI products is a must. Past experience as a data scientist is a plus.
- Has knowledge and experience of working on Agentic AI powered systems such as AI Agents, AI Assistants, AI Copilots.
- Has knowledge and experience of managing AI / ML features and models through their complete life\-cycle \- from conducting structured experimentation to deploying in production to maintaining their efficacy in production.
- Proven ability to create \& drive roadmap and strategy for AI focused offerings \- especially platforms and products such as AI Agents, AI Assistants.
- Ability to drive customer adoption and engagement; leverage analytical tools and customer feedback for insight driven initiatives
- Self\-starter with a passion for independent, creative problem\-solving, strong ownership, high commitment and a strong business judgment.
- Strong validated experience with leadership, business insight, problem\-solving, critical thinking, and analytical abilities.
- Shown experience working in a complex, multiple BU environment, handling multiple stakeholders.
- Bachelor's degree in Business, Computer Science, or related field. MBA preferred.
Additional Information Please note this is a hybrid role with onsite expectations of 3x/week (Tues \- Thurs) from our San Mateo, CA headquarters.
The annual base salary range for this position is $241,000 \- $298,000\. This role is also eligible for a target bonus.
Compensation is based on a variety of factors, including but not limited to location, experience, job\-related skills, and level.
Freshworks offers multiple options for dental, medical, vision, disability, and life insurance. Equity \+ ESPP, flexible PTO, flexible spending, commuter benefits, and wellness benefits are also offered. Freshworks also offers adoption and parental leave benefits.
At Freshworks, we have fostered an environment that enables everyone to find their true potential, purpose, and passion, welcoming colleagues of all backgrounds, genders, sexual orientations, religions, and ethnicities. We are committed to providing equal opportunity and believe that diversity in the workplace creates a more vibrant, richer environment that boosts the goals of our employees, communities, and business. Fresh vision. Real impact. Come build it with us.
Salary Context
This $241K-$298K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At freshworks, 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. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($269K) sits 23% above the category median. Disclosed range: $241K to $298K.
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.
freshworks AI Hiring
freshworks has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in San Mateo, CA, US. Compensation range: $200K - $298K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 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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