Interested in this AI/ML Engineer role at Didero?
Apply Now →About This Role
At Didero, we’re building the autonomous supply chain, starting with agentic supplier management.
Global trade has never been more complex or more critical. Teams are underwater, reacting to a flood of challenges — from geopolitical risk to tariffs. Didero helps by automating time\-intensive workflows with AI agents, deploying cutting\-edge technology into one of the world’s most vital domains.
We’re backed by some of the world’s best venture funds and leading figures across AI, supply chain, and enterprise software.
About the role
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As an AI Deployment Strategist, you'll help our customers transform how procurement teams work by deploying AI agents into real\-world operations. You'll partner directly with customers to understand their workflows, configure AI agents, improve automations, and continuously optimize performance as customer needs evolve.
You'll work directly with customer stakeholders to understand their business processes, map operational workflows, design implementation plans, and guide deployments from kickoff through long\-term success. This role sits at the intersection of strategy, operations, AI, and product, translating complex customer challenges into scalable AI\-powered solutions.
This is not a traditional customer success role; it's the evolution of it. You'll operate at the intersection of customer strategy, AI, product, and operations, working closely with Product and Engineering to shape both our customers' success and the future of the platform. This role is hands\-on and technical: you'll be directly configuring, testing, and tuning AI agents — not just managing the relationship.
We're looking for someone with the instincts of a strong project manager: someone who can walk into a customer's messy process, dig into the details, map it out precisely, and run a tight, structured implementation, not someone whose background is primarily relationship management.
What You’ll Do
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- Own the end\-to\-end customer journey post\-sale, from deployment scoping through full adoption and value realization
- Lead AI agent implementations and onboarding from kickoff through production, translating customer supply chain workflows into scalable automations
- Dig into customer processes in detail, understand the workflow, map it out, and identify exactly where and how automation should be applied
- Develop deep expertise in how Didero's agents work in production, configure, test, and tune agent behavior directly, without always waiting on an engineering sprint
- Act as a strategic advisor to procurement and operations stakeholders, identifying where expanded automation can drive the most ROI
- Partner closely with Product and Engineering to surface customer\-driven insights that shape the roadmap and improve real\-world agent performance
- Track and make explicit the business outcomes Didero's agents deliver cost savings, cycle time reduction, supplier risk mitigation to anchor retention and expansion conversations
- Identify and develop expansion opportunities within your book of business, partnering with Sales on renewals and upsell
- Build repeatable implementation playbooks and best practices as the team scales
You’ll be successful in this role if you…
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- Think like a builder, not just a relationship manager. You want to get inside the product and understand how it actually works.
- Are exceptional at running tight processes: structuring ambiguous problems, mapping workflows step by step, and driving a deployment to a clean finish.
- Are energized by working directly with AI products and experimenting with new capabilities.
- Love solving ambiguous customer problems with technology.
- Have a strong instinct for where AI can drive ROI in complex operational workflows.
- Thrive in fast\-moving startup environments and default to action over deliberation.
- Can simplify complex concepts for non\-technical audiences and proactively surface opportunities before customers ask.
Qualifications
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- 4–7\+ years of experience in management/strategy consulting, project/program management, or customer success, with demonstrated success leading complex customer\-facing initiatives
- Strong project management skills with the ability to coordinate multiple stakeholders and drive execution, this is the single most important skill for this role
- Comfortable getting up to speed quickly on APIs, integrations, and agentic AI, you don't need this experience on day one, but you should be excited to learn it fast
- Proven experience leading complex customer deployments from kickoff through full adoption
- Strong communicator with experience presenting to senior stakeholders and navigating large organizations
Bonus Points For:
- Background in procurement, supply chain, ERP systems, or enterprise operations.
- Prior experience at a top\-tier consulting firm (e.g., MBB, Big 4 strategy arm) or in a Forward Deployed Engineer–style role.
- Familiarity with workflow automation platforms or APIs.
- Startup experience in high\-growth B2B SaaS.
What We Value
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- We make the world feel smaller \- We support each other and break down boundaries, both internally and for our customers
- We build supply chain magic \- We push the boundaries of what technology can do and go above and beyond for our users
- We are lifelong learners \- We move fast, embrace failure, and give feedback generously
- We show up \- We’re scrappy entrepreneurs, no job is beneath us
- We BELIEVE! \- We’re building a great, enduring company and we have fun doing it.
What We Offer:
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- Competitive compensation and equity
- Medical, dental, and vision insurance
- Unlimited PTO — we trust you to take the time you need
- Monthly wellness stipend via ClassPass
- Tech equipment for your setup
- Catered lunches (for those in the office)
Compensation Range: $100K \- $200K
Salary Context
This $100K-$200K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Didero, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($150K) sits 31% below the category median. Disclosed range: $100K to $200K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Didero AI Hiring
Didero has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $200K - $200K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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