Interested in this AI/ML Engineer role at Mirakl - Labs?
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About This Role
About Mirakl:
Founded in 2012, Mirakl has been at the forefront of marketplace innovation, empowering every business to compete in the platform economy.
Today, Mirakl's operating system combines an enterprise marketplace solution (Mirakl Platform) that enables retailers and B2B organizations to launch, scale, and operate marketplaces and dropship, AI\-powered multichannel selling (Mirakl Connect), retail media (Mirakl Ads) and an agentic commerce infrastructure (Mirakl Nexus).
With dual headquarters in Boston and Paris, Mirakl helps a global ecosystem of 450\+ marketplaces (B2C and B2B) and a network of over 100k third\-party marketplace sellers. Brands like Macy's, Decathlon, Carrefour, Asos, and Airbus Helicopters use Mirakl to grow their businesses in new and remarkable ways.
For more information: www.mirakl.com.
Mirakl in Numbers:
- ️ Founded in 2012 \| Member of French Tech Next40
- 750\+ employees in 9 offices worldwide: Paris, Barcelona, Bordeaux, Boston, London, Munich, New York, Sydney, Tokyo
- 350\+ Mirakl Tech teams members mainly based in France
- + ️ 5 Saas Solutions
Our Values:
Working at Mirakl means accelerating your career alongside ambitious, passionate, and supportive colleagues. We're proud of the diversity of backgrounds, perspectives, and experiences that make our teams unique.
Our 5 values guide how we collaborate:
- Work Hard Together: Teamwork and collaboration are the foundation of our success
- Get Things Done: We prioritize action and efficiency for impactful results
- Go Above \& Beyond: We tackle challenges proactively and always aim for excellence
- Succeed Through Expertise: Knowledge sharing and continuous learning are core to our culture
- Satisfy \& Empower Clients: We're committed to our clients' success
### Mission
Lead the design, development, and adoption of AI systems within a strategic business domain. You will be part of the AI team, working inside one of our division (Marketing or finance for example) to lead their AI transformation.
You will combine deep AI engineering expertise with product and business understanding to identify high\-impact opportunities, define roadmaps, and deliver measurable outcomes.
As the AI technical lead for your domain, you will drive architecture decisions, engineering excellence, and AI adoption across teams and stakeholders. Examples of domains include: Sales, GTM and Marketing or Finance \& Legal
This is a permanent position (CDI) based in our Boston office, with 4 days on\-site per week.
### Why Now, Why Mirakl
Generative AI and AI Agents are fundamentally reshaping commerce software.
At Mirakl, AI is becoming a core part of our product strategy. From Catalog Transformer and content generation to AI Agents and Agentic Commerce, we are building the next generation of commerce software.
We are looking for a Senior AI Engineer to transform cutting\-edge AI technologies into reliable, scalable, and measurable products used by leading enterprises worldwide.
### You won't be joining a company that's thinking about AI. You'll be joining one that's already building it.
### What You'll Do
#### 1\. Own AI Strategy for Your Domain
- Define the AI vision and roadmap for your domain
- Identify high\-value opportunities with Product Managers and business leaders
- Prioritize investments based on business impact
#### 2\. Lead AI System Development
- Design and build production\-grade AI systems
- Drive architecture decisions
- Ensure scalability, reliability, and maintainability
#### 3\. Drive Adoption \& Business Impact
- Work closely with end users
- Measure adoption and outcomes
- Accelerate AI transformation across your domain
#### 4\. Establish Technical Excellence
- Promote best practices for evaluation, testing, guardrails, and observability
- Review designs and mentor engineers
- Raise the quality bar across initiatives
#### 5\. Grow AI Capability
- Mentor AI Engineers and Agent Builders
- Share knowledge and reusable components
- Build AI expertise within your organization
### Success Metrics
- Business impact delivered by AI initiatives
- Adoption within the domain
- Technical quality and reliability
- Team capability growth
### Profile
- 7\+ years in Software Engineering, ML Engineering, or Applied AI
- Strong experience with LLMs, RAG, AI Agents, evaluation, and production systems
- Ability to influence Product and Business decisions
- Proven leadership on complex cross\-functional initiatives
Tech Stack
- Languages: Python and SQL
- AI Stack: OpenAI, Anthropic, Gemini, Mistral and Open\-source models
- Agentic \& Retrieval: LangGraph, LangChain, LlamaIndex, MCP and Vector Databases
- Infrastructure: AWS or GCP, Docker and Kubernetes
Feel free to add a link to your GitHub in your CV so we can understand what you have worked on!
\#LI\-Hybrid
*We welcome collaborators with their diverse perspectives and experiences to power us forward. These often far exceed conventional job requirements and help us create a culture of continuous learning. If you're ready to join a global leader powering digital transformation for 450\+ of the world's most innovative retailers and B2B organizations.*
*As part of our recruitment process, Mirakl processes your personal data to review and manage your application and, where appropriate, to consider your profile for future opportunities. You can exercise your data protection rights at any time, and as further detailed in our policies. For more information about how we process your personal data and your rights, please consult our Recruitment Privacy Notice,* *here* *in English and* *here* *in French.*
*We may use Artificial Intelligence (AI) solutions to help streamline our hiring process, including screening applications, analyzing resumes, and assessing responses. While AI helps us work efficiently, all final hiring decisions are made by humans. For more information, visit our* *AI Guidelines for Candidates and Interviews**.*
Salary Context
This $150K-$190K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Mirakl - Labs, 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 $185,000 based on 13,200 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($170K) sits 8% below the category median. Disclosed range: $150K to $190K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Mirakl - Labs AI Hiring
Mirakl - Labs has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US. Compensation range: $190K - $190K.
Location Context
AI roles in Boston pay a median of $216,350 across 460 tracked positions. That's 8% above the national 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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