Interested in this AI/ML Engineer role at Didero?
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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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We're hiring an AI Applied Engineer to sit at the intersection of machine learning, agentic systems, and production software. You'll own the design and development of AI\-powered workflows that automate high\-stakes supply chain operations — building systems that actually think, act, and learn
What You’ll Do
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- Design and ship production AI agents that automate supplier management workflows
- Own the full AI stack: model selection, prompt engineering, tool use, RAG pipelines, evals, and reliability.
- Architect agentic systems that integrate with communication platforms, ERPs, and third\-party APIs.
- Drive quality through rapid iteration, build fast without shipping fragile systems.
- Collaborate with product and domain experts to translate messy real\-world workflows into clean, scalable AI solutions.
- Shape how we approach applied AI at the company level, your opinions on architecture and methodology will matter.
You’ll be successful in this role if you…
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- Have hands\-on experience building and deploying LLM\-based applications or AI agents in production.
- Think in systems; you understand how prompts, retrieval, memory, and tool calls interact at scale.
- Care about outcomes, not just outputs, you build evals and measure what matters.
- Are comfortable in ambiguous, fast\-moving environments with short feedback loops.
- Write clean, maintainable code across the backend and can collaborate on frontend integrations.
Qualifications
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- 8\+ years as an autonomous, resourceful engineer building and scaling real\-world applications.
- Proven experience with LLMs, agentic frameworks (Pydantic, LangChain, LlamaIndex, custom), and AI workflow tooling.
- Strong Python (Django or FastAPI) and familiarity with TypeScript / React for integration work.
- Deep understanding of modern AI primitives: function calling, RAG, embeddings, structured outputs, evals.
- Strong communication skills, able to bridge technical and non\-technical stakeholders effectively.
Bonus Points For:
- Startup experience; founding engineer or early team member at a high\-growth company.
- Experience with B2B SaaS workflow automation and complex enterprise integrations.
- Background in supply chain, procurement, or logistics tech.
- Designing highly scalable and distributed AI systems.
- Experience with a strongly typed language like C\+\+, Java, Rust, Go
- Has a strong understanding of system design, object oriented programming and good programming fundamentals.
Tech Stack
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Django · Python · React · TypeScript
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: $200K \- $300K
Salary Context
This $200K-$300K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($250K) sits 38% above the category median. Disclosed range: $200K to $300K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; 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: $300K - $300K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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