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About This Role
Location
Remote
Employment Type
Full time
Location Type
Remote
Department
Engineering
About Us
Launched in 2019, Constructor is an AI-first ecommerce search and discovery platform that helps shoppers find the right products at the right time and enables leading global e-commerce brands to drive meaningful revenue and conversion gains.
About the Role
As an Engineering Manager on the Agentic Integrations team, you’ll lead engineers building and scaling integrations for Constructor’s agentic products across our customer base. This team sits within the ML Journey chapter and operates at the intersection of customer needs, applied ML, product engineering, and production integration work.
Your team will work directly with customers and internal teams to bring agentic capabilities into real-world retail workflows, helping customers adopt, configure, extend, and scale these products in their environments. The work spans two fronts: delivering high-quality customer integrations for agentic products, and turning repeated implementation patterns into reusable tooling, frameworks, documentation, and platform capabilities that make future integrations faster and more reliable.
You’ll be responsible for setting the technical and operational direction for the team: how we engage customers, how we make integration work scalable, how we maintain quality across a growing number of deployments, and how we turn customer-specific learnings into product and platform improvements. Success in this role requires strong engineering leadership, customer empathy, excellent prioritization, and the ability to work effectively across Product, ML, Customer Success, Solutions, and core Engineering teams.
Challenges you will tackle
- Scaling agentic integrations: Be a part of building a new team that can take agentic capabilities from promising product functionality to reliable, production-grade customer deployments across many different customer environments.
- Balancing customer delivery and scalable foundations: Allocate the team’s capacity across hands-on customer integration work and longer-term investments in reusable tooling, templates, documentation, automation, and platform improvements.
- Cross-functional collaboration and prioritization: Work closely with Product, ML, Customer Success, Solutions, and other Engineering teams to prioritize customer needs, unblock adoption, and ensure integration learnings feed back into the product roadmap.
- Technical direction and quality: Set a high bar for architecture, testing, observability, reliability, and maintainability in customer-facing integrations, especially where agentic systems interact with customer data, workflows, and business-critical experiences.
- Operational excellence in customer work: Create lightweight but disciplined processes for scoping integrations, managing commitments, handling escalations, tracking risks, and ensuring customers receive a consistent, high-quality experience.
- Developer and implementation experience: Improve the internal and external developer experience for agentic integrations by driving clear documentation, reference implementations, reusable components, integration playbooks, and debugging tools.
- Recruiting and hiring: Own the hiring process for your team end to end, from defining the right profile for forward-deployed engineers to building a strong pipeline, running effective interview loops, and closing candidates.
- Career growth: Coach and develop engineers who need to be strong both technically and in customer-facing contexts, giving direct feedback, creating growth opportunities, and helping them navigate ambiguity.
Requirements
- 4+ years of software engineering experience (directly leading an engineering team is a huge plus)
- Experience in JavaScript, Front-End, FullStack development
- Strong technical background in backend, full-stack, platform, or applied ML product engineering
- Experience leading teams that build customer-facing, production-grade systems or integrations
- Strong analytical and problem-solving skills, with the ability to triage ambiguous customer issues and translate them into clear technical plans
- Comfort working directly with customers, including technical discovery, scoping, expectation-setting, troubleshooting, and communicating tradeoffs
- Ability to collaborate effectively with Product, ML/Data Science, Customer Success, Solutions, and Engineering stakeholders
- Strong written and verbal communication skills, including the ability to explain complex technical topics to both technical and non-technical audiences
- Good product judgment and customer empathy, with a bias toward solving real customer problems while preserving long-term technical leverage
- Experience with LLMs, agentic systems, ML-powered products, recommendations, search, personalization, or data-heavy SaaS products is a strong plus
- Experience building reusable tooling, frameworks, SDKs, integration templates, or internal platforms is a plus
- English on B2 level (CEFR) or higher
- Able to attend regular meetings between 1300 and 1700 UTC
- Passion to help fuel the continued success of our high-growth startup
Benefits
- ️ Unlimited vacation time - we strongly encourage all employees to take at least 3 weeks per year
- 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 - refreshed each year for every employee
- Maternity & Paternity leave for qualified employees
- Work with smart people who will help you grow and make a meaningful impact
- Base salary: $100k–$130k USD, depending on knowledge, skills, experience, and interview results
- Stock options - offered in addition to the base salary
- Regular team offsites to connect and collaborate
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.
Studies have shown that women and people of color may be less likely to apply for jobs unless they meet every one of the qualifications listed. Our primary interest is in finding the best candidate for the job. We encourage you to apply even if you don’t meet all of our listed qualifications.
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.
Salary Context
This $100K-$130K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 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 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($115K) sits 46% below the category median. Disclosed range: $100K to $130K.
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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