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Location
Remote - EMEA
Employment Type
Full time
Location Type
Remote
Department
ProductProduct Management
Staff Product Manager, Agentic Discovery
About Us Constructor powers product search and discovery for some of the largest retailers in the world. We serve billions of requests every week, and you've probably seen our results somewhere and used our product without knowing it. We differentiate ourselves by focusing on metrics over features, and reinventing search and discovery from the ground up as a machine learning challenge with the specific goal of improving metrics like revenue. We're approximately doubling year over year despite the market slowing down and have customers in every eCommerce vertical. We're a passionate team of technologists who love solving problems and want to make our customers' and coworkers' lives better. We value empathy, openness, curiosity, continuous improvement, and are excited by metrics that matter. We believe that empowering everyone in a company to do what they think is best can lead to great things.
Job Summary We're looking for a systems-minded, AI-native Staff Product Manager to help lead the next chapter of Constructor: Agentic Discovery. As commerce shifts toward agentic experiences, we're investing heavily in a portfolio of AI-powered surfaces — our AI Shopping Assistant, Product Insights Agent, Prism recommendations, and more to come — that change how shoppers discover products and how businesses run discovery.
This is a high-leverage, high-ambiguity role. You'll own two things at once: helping our agentic experiences scale from promising experiments into a repeatable, reliable engine, and giving the business teams who depend on them the same transparency and control over agentic experiences that they have today over traditional search.
You'll start by owning the business tooling and dashboards for agentic discovery — the experiences that let business teams see what our agents are doing, why, and to what effect, so they can execute faster and steer with confidence. Over time, your mandate grows to setting direction for the entire Agentic Discovery chapter: the on-site agentic experiences, the offsite/agentic surfaces, the integrations that connect them, and the agentic tooling underneath. This is a Staff role today with a clear path to Director as you take on chapter-wide leadership.
Agentic discovery at Constructor is fundamentally an AI and machine learning problem, so this role lives and breathes it. You'll partner closely with ML, engineering, design, and go-to-market to turn new agentic capabilities into experiences that ship, prove value, and stay under our customers' control — and serve as the connective tissue that aligns multiple teams behind one coherent vision for agentic discovery.
If you're excited about defining a brand-new category, being held to real usage and business outcomes, and giving businesses control over the AI experiences shaping their storefronts, this role is for you.
What You'll Do
- Own business impact, not features. Drive measurable lift in usage and business outcomes — conversion, revenue, engagement — across Constructor's agentic experiences.
- Give businesses transparency and control. Build the dashboards and tooling that let business teams see what agentic experiences are doing, understand why, and steer them — closing the gap to the control they have in traditional search.
- Scale experimentation. Turn one-off agentic experiments into a repeatable, scalable way to launch, measure, and improve new agentic interfaces — cutting time-to-launch.
- Set chapter vision and strategy. Define the multi-quarter direction for Agentic Discovery — where we're heading and why — tie it to customer business outcomes, and rally teams behind it.
- Lead the business-tooling and dashboard experience first. Own its strategy and roadmap, and make agentic behavior and impact visible and actionable for business users.
- Orchestrate across surfaces. Unite on-site agentic experiences, offsite/agentic surfaces, and integrations behind one definition of agentic discovery success, so improvements compound instead of colliding.
- Turn AI capability into shipped experiences. Partner with ML and engineering to take agentic and LLM-powered experiences from idea to production, balancing quality, latency, cost, and reliability.
- Make success measurable. Define and instrument the metrics — go-live velocity, usage, engagement, business outcomes — that tell us whether agentic discovery is working.
- Keep it trustworthy. Treat explainability, control, and guardrails as first-class requirements so customers trust and adopt agentic experiences.
- Grow the mandate. Expand from business tooling into chapter-wide leadership across the full agentic portfolio and the integrations beneath it.
What Success Looks Like Within your first year, you will have:
- Shipped a dashboard and tooling experience that gives business teams real transparency into and control over Constructor's agentic experiences, adopted by internal teams and customers.
- Driven measurable lift in the usage and business impact (conversion, revenue) of agentic experiences across multiple customers.
- Stood up a repeatable process for launching and experimenting with new agentic interfaces, measurably shortening time-to-launch.
- Defined the core metrics for agentic discovery and made them visible in one place.
- Begun uniting the on-site, offsite, and integrations teams behind one coherent direction for the chapter.
What We're Looking For
- Extensive product management experience (typically 8+ years) in AI/ML, agentic, search, or discovery products, including time operating at a senior or staff level.
- A track record of moving business metrics at scale — usage, conversion, revenue, engagement — not just shipping features, and of setting strategy across multiple teams.
- Experience building for business users/operators (merchandisers, category managers, or similar) — especially the tooling, dashboards, and controls they rely on.
- Strong AI/ML/LLM product depth — understands how agentic and LLM-powered experiences work and can reason about their tradeoffs (quality, latency, cost, reliability).
- Comfort with zero-to-one ambiguity and with scaling — able to experiment on brand-new interfaces and to build the repeatable process that turns them into a portfolio.
- A rigorous, measurement-first mindset that ties every experience back to usage and business outcomes.
- Excellent cross-functional leadership — able to align teams with different goals behind a shared definition of success, working shoulder-to-shoulder with ML, engineering, design, and go-to-market.
- A strong instinct for trust — treating transparency, control, and guardrails as core to whether agentic experiences succeed.
- Comfort setting direction where none exists yet, and leading through clarity and credibility rather than the org chart.
Benefits
- Unlimited vacation time — we strongly encourage all of our employees to take at least 3 weeks per year.
- A competitive compensation package including stock options.
- Company-sponsored US health coverage (100% paid for employee).
- Fully remote team — choose where you live.
- Work-from-home stipend! We want you to have the resources you need to set up your home office.
- Apple laptops provided for new employees.
- Training and development budget for every employee, refreshed each year.
- Parental leave for qualified employees.
- Work with smart people who will help you grow and make a meaningful impact.
This position is fully remote — Constructor.io is a remote-first company.
Diversity, Equity, and Inclusion at Constructor At Constructor.io, we are committed to cultivating a work environment that is diverse, equitable, and inclusive. As an equal opportunity employer, we welcome individuals of all backgrounds and provide equal opportunities to all applicants regardless of their education, diversity of opinion, race, color, religion, gender, gender expression, sexual orientation, national origin, genetics, disability, age, veteran status, or affiliation in any other protected group.
How we handle your data. By submitting this application, you acknowledge that Constructor will collect and process the personal data you provide for the purpose of evaluating your candidacy for this role. If you are not selected, we will retain your data for a limited period as described in our Recruitment Privacy Notice. After that period your data will be deleted or anonymized.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Constructor, 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 in Demand for This Role
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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Constructor AI Hiring
Constructor has 7 open AI roles right now. They're hiring across AI Agent Developer, AI/ML Engineer. Based in Remote, US. Compensation range: $120K - $130K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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