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About This Role
Who We Are
At Bluehost, we believe all small businesses deserve the opportunity to succeed online. Our mission is to give small businesses the confidence to think big. We're the all\-in\-one platform for small businesses to grow online with powerful simplicity, premium performance, smart tools and human support.
We are entering the agentic era. Our customers — small business owners and the professionals who serve them — will increasingly get work done by delegating to agents rather than clicking through screens. Across the company we are building a growing portfolio of business and product agents: agents that build and optimize websites, agents that market, agents that support and retain, agents that sell, agents that manage the account. Individually they are powerful. Stitched together, they become the product.
That stitching is this role. The Principal PM, Agentic Experiences owns the connective layer — how a fleet of independently\-built agents shows up to the customer as one coherent, trustworthy, frictionless experience, across every interface we expose: the customer portal, our APIs, MCP servers, and third\-party connectors and channels.
This is a highly cross\-functional, high\-visibility individual contributor role for someone who thinks in systems, has strong product taste, and can operate in a product\-led organization where influence matters more than reporting lines.
What you’ll do \& how you’ll make your mark.
The agentexperiencearchitecture. Define the end\-to\-end model for how agents are discovered, invoked, handed off between, and held accountable to the customer. Own the shared primitives — identity and permissions, context and memory, intent routing, hand\-off and escalation, human\-in\-the\-loop approval, transparency and traceability, guardrails and fallback.
Connectingthe dots to the customer. Translate a portfolio of internally\-owned agents into a single customer narrative: what the customer can ask for, what happens when they do, how they know it worked, and what it costs. Own the customer journey across agents, not just the agents.
The interface surface strategy. Set the strategy for how agentic capability is exposed across:
- Portal — conversational and ambient agent surfaces inside the logged\-in experience, alongside and eventually in place of traditional navigation
- APIs — clean, agent\-consumable contracts over our commerce, hosting, domains, and account systems
- MCP — our capabilities as tools that both our own agents and external agents (Claude, ChatGPT, IDEs, partner platforms) can call safely
- Connectors \& channels — bringing our agents to where the customer already works, and bringing their tools into ours
Standards andthe operatingmodel. Establish the shared patterns, quality bars, and interaction standards that let a dozen teams ship agents independently without fragmenting the experience. Define what "good" looks like — latency, accuracy, containment, escalation, tone, disclosure — and the review mechanism that holds it.
Measurement. Define the metric framework for agentic experience: task success and completion rate, hand\-off and containment, time\-to\-value, trust and intervention rates, and the downstream commercial impact on conversion, attach, and retention.
Roadmap and influence. Build the multi\-quarter roadmap for the connective layer, and drive alignment across product, engineering, data, design, care, and marketing to sequence it. Partner especially closely with Data \& Personalization — agents are only as good as the context and signals they act on.
Who you are \& what you’ll need to succeed.
- 10\+ years in product management, including significant time on platform, API, or ecosystem products, and time on customer\-facing experiences. You have shipped both.
- Demonstrated ownership of an AI or agentic product surface — assistants, copilots, automation, orchestration, or tool\-calling systems in production, with real users and real accountability for outcomes.
- Deep technical fluency. You can hold a credible design conversation about LLM orchestration, tool and function calling, MCP, RAG and context strategy, evals, latency and cost trade\-offs, and where the failure modes live. You do not need to write the code; you do need to know what you're asking for.
- API and platform product judgment. You understand what makes an interface a good contract, and you have opinions about versioning, permissions, and developer experience.
- Systems thinker,experience\-obsessed. You naturally see the seams between products — and you care, viscerally, when a customer hits one.
- Exceptional influence without authority. You can align senior stakeholders across a matrixed, product\-led organization, and drive convergence on standards teams actually adopt.
- Strong written communication. You write the doc that makes twelve teams agree.
- Bias to ship. You'd rather land a real, narrow agentic journey end\-to\-end this quarter than a perfect framework next year.
Nice tohave: experience in hosting, domains, web presence, SMB SaaS, or e\-commerce; experience with agent evaluation and observability tooling; experience building or publishing MCP servers or public developer platforms.
What Success Looks Like:
- First90 days — a shared, agreed map of every agent in flight across the business; a documented target\-state agent experience architecture; one prioritized customer journey selected for end\-to\-end stitching.
- Six months — that journey shipped across at least two interfaces (portal plus API or MCP), with a live measurement framework and adopted interaction standards.
- Twelve months — a functioning connective layer other teams build on by default, measurable improvement in task success and time\-to\-value, and a credible external agent story for partners and third\-party platforms.
The target compensation range for this position is $150,000 to $190,000 annually. Individual salaries are determined by various factors including, but not limited to: candidate’s qualifications, such as skills, education, and experience, as well as internal equity and market conditions.
\#LI\-SM1 \#LI\-Remote \#Bluehost
Employment with Newfold Digital is at\-will and nothing in this Job Description should be interpreted or construed to alter the at\-will employment relationship.
Salary Context
This $150K-$190K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Newfold Digital, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($170K) sits 21% below the category median. Disclosed range: $150K to $190K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Newfold Digital AI Hiring
Newfold Digital has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $190K - $190K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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