Interested in this AI/ML Engineer role at HelloFresh?
Apply Now →Skills & Technologies
About This Role
As the Associate Director of Paid Search, you will lead the strategic direction and performance of one of the most critical acquisition channels across the United States. Operating within a global, matrixed organization, you will act as the key bridge between centrally defined global strategy and local market execution.
You will be responsible for ensuring that global Paid Search roadmaps, frameworks, and best practices are effectively translated, localized, and executed to meet regional business objectives. At the same time, you will advocate for the United States market needs, influencing global stakeholders to continuously refine and evolve the overarching strategy.
Reporting to the Director of Growth Marketing, you will oversee a high\-performing team and a significant marketing budget, driving high\-impact execution against ambitious company goals.
This is a leadership role requiring cross\-functional collaboration with Adtech, Creative, Analytics, and SEO teams to find synergies and bring high\-quality customers to the business. You will own the long\-term roadmap and ensure excellence across our meal kit brand portfolio, including Factor, HelloFresh, Green Chef, and EveryPlate.
#### You will …
- Global–Local Strategy Alignment: Own the adaptation and execution of the global Paid Search roadmap for the United States
- Strategic Leadership: Act as the primary interface between global marketing teams and local stakeholders. Provide structured feedback loops, influencing roadmap evolution based on local insights
- Market Expert: Ensure alignment between centrally defined strategies and regional performance goals
- Team Management: Lead, mentor, and grow a high\-performing team of search specialists and managers.
- Budget \& Performance: Take full accountability for campaign management, budgeting, and optimization across all search portfolios.
- Advanced Analytics: Utilize automated dashboards and post\-analysis reports to turn complex data into actionable insights for executive stakeholders. Ensure data\-driven decisions are communicated clearly across teams.
- Hands\-On Reporting: Independently build and manipulate spreadsheets (Excel, Google Sheets, or internal tools like Gemini) to perform deep\-dive analyses, model scenarios, and generate ad hoc insights. Translate findings into clear, visual presentations to guide strategic decisions and support your team.
- Testing \& Innovation: Partner closely with the global AdTech/Product Innovation lead to implement best practices, pilot new tools and methodologies, and provide market feedback to inform future product and innovation roadmaps.
- Cross\-Functional Synergy: Partner with Brand, BI, CRM, and SEO teams to optimize the full consumer journey and provide strategic feedback on creative direction and ad copy.
#### You are…
- Growth\-Obsessed: A leader who thrives in fast\-paced environments and is not afraid to dive into the details when necessary.
- A Strategic Thinker: Capable of deducing consumer signals and search engine evaluation trends to stay ahead of the curve.
- An Expert Communicator: Able to explain complex rationales and data trends to both technical teams and senior leadership.
- Analytically Gifted: Possessing superior analytical skills to optimize day\-to\-day performance and drive large\-scale hypothesis testing.
- A Proven Leader: A natural "hustler" with a "can\-do" attitude and a track record of driving high\-performance results.
#### You have…
- Experience: 8\+ years of experience in Paid Search (agency or client\-side), with at least 5\+ years in team management and leadership roles.
- Education: A bachelor's degree with a strong academic record or proven relevant commercial experience.
- Technical Mastery: Deep expertise in search bidding strategies (tCPA, tROAS), auction dynamics, and various campaign types (Shopping, App, Discovery).
- Platform Proficiency: Google and Bing Ads certification is required; familiarity with tag management and app tracking troubleshooting is essential.
- Operational Excellence: Strong organizational and project management skills with the ability to own large\-scale initiatives from start to finish.
You'll get…
- Competitive salary, 401k with company match that vests immediately upon participation
- Generous PTO, including sabbatical, and parental leave of up to 16 weeks
- Comprehensive health and wellness benefits with options at $0 monthly, effective first day of employment
- Tuition reimbursement for continuing education (upon 2 years of service)
- Up to 85% discount on subscriptions to HelloFresh meal plans (HelloFresh, Green Chef, Everyplate, and Factor\_)
- Access to Employee Resource Groups that are open to all employees, including those pertaining to BIPOC, women, veterans, parents, and LGBTQ\+
- Inclusive, collaborative, and dynamic work environment within a fast\-paced, mission\-driven company that is disrupting the traditional food supply chain
This job description is intended to provide a general overview of the responsibilities. However, the Company reserves the right to adjust, modify, or reassign work tasks and responsibilities as needed to meet changing business needs, operational requirements, or other factors.
Salary Context
This $161K-$188K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At HelloFresh, 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 $166,983 based on 13,781 positions with disclosed compensation. Director-level AI roles across all categories have a median of $244,288. This role's midpoint ($175K) sits 5% above the category median. Disclosed range: $161K to $188K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
HelloFresh AI Hiring
HelloFresh has 4 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $88K - $188K.
Location Context
AI roles in New York pay a median of $200,000 across 1,670 tracked positions. That's 9% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.