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About This Role
This is an incredible opportunity to be part of a company that has been at the forefront of AI and high\-performance data storage innovation for over two decades. DataDirect Networks (DDN) is a global market leader renowned for powering many of the world's most demanding AI data centers, in industries ranging from life sciences and healthcare to financial services, autonomous cars, Government, academia, research and manufacturing. "DDN's A3I solutions are transforming the landscape of AI infrastructure." – IDC *“The real differentiator is DDN. I never hesitate to recommend DDN. DDN is the de facto name for AI Storage in high performance environments” \- Marc Hamilton, VP, Solutions Architecture \& Engineering \| NVIDIA*
DDN is the global leader in AI and multi\-cloud data management at scale. Our cutting\-edge data intelligence platform is designed to accelerate AI workloads, enabling organizations to extract maximum value from their data. With a proven track record of performance, reliability, and scalability, DDN empowers businesses to tackle the most challenging AI and data\-intensive workloads with confidence.
Our success is driven by our unwavering commitment to innovation, customer\-centricity, and a team of passionate professionals who bring their expertise and dedication to every project. This is a chance to make a significant impact at a company that is shaping the future of AI and data management.
Our commitment to innovation, customer success, and market leadership makes this an exciting and rewarding role for a driven professional looking to make a lasting impact in the world of AI and data storage.
We’re looking for a Senior Software Engineer to build internal applications on top of DDN’s enterprise data platform. This is a largely greenfield charter — a new function dedicated to full\-stack tools and AI\-powered services for GTM, Finance, Support, and Product. We are building applications that surface data for decision\-making *and* applications that improve and automate the operational processes that run the business. You’ll have early prototypes to learn from, but the mandate is to define this product portfolio and build it out. Data and analytics engineers own what’s underneath; the applications themselves — frontend, backend, deployment, model integration — are yours.What You’ll Own
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- Internal applications — design, build, and operate full\-stack web apps (FastAPI/Flask \+ React/TypeScript today, but technology choices are open) that put data and AI into stakeholders’ hands — both as decision\-support interfaces and as purpose\-built tools that let them do operational work
- AI/LLM integration — build features powered by LLMs and ML — classification, extraction, summarization, copilots, agentic workflows — choosing whichever models, providers, and frameworks fit the problem
- Application infrastructure — deploy and operate apps on GCP (App Engine, Cloud Run, GKE), connect them to the data platform, manage auth, own CI/CD and app security
- Product surface — define what good looks like for this new function: which problems are worth a custom app vs. a BI dashboard, what our reusable building blocks should be, and how we ship reliable, observable services people depend on
- Collaboration — partner with stakeholders to scope the right tool for the job, with analytics engineers to shape the underlying data models, and with data engineers on platform constraints
Your Experience Includes
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- 5\+ years building production software, with meaningful time spent on full\-stack web applications
- Strong Python — APIs (FastAPI, Flask, or similar), data access patterns, packaging, testing
- TypeScript/React (or comparable framework), component design, interactive data UIs
- Hands\-on experience with GCP application services — App Engine, Cloud Run, GKE, IAM
- Strong SQL and comfort working with cloud data warehouses (BigQuery in our case) — you can write a query, understand its cost, and design an app’s data access layer around it
- Experience developing and deploying AI/LLM\-powered applications in production — prompt design, structured output, evaluation, cost/latency tradeoffs, awareness that the model and tooling landscape changes quickly
- Experience operating what you ship — logging, monitoring, error handling, debugging in production
- Experience with software engineering best practices: CI/CD, automated testing, observability, secure application design
- Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience
Nice to Have
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- Experience building AI\-native applications such as text\-to\-SQL interfaces, copilots, agentic workflows, or automated insight\-generation systems
- Hands\-on experience with one or more LLM provider APIs (Anthropic’s Claude, OpenAI, Google, open\-weight models, etc.) and agent frameworks (Claude Agent SDK, LangGraph, or similar)
- Experience with managed AI/ML platforms (Vertex AI, SageMaker, or similar) — model serving, embeddings, evaluation tooling
- Familiarity with dbt and modern data warehouse patterns from a consumer’s perspective
- Experience with Airflow for triggered jobs and background work
- Familiarity with Terraform for managing application infrastructure
- Background designing data\-heavy UIs — tables, drill\-downs, large result sets, interactive exploration
- Prior experience as the first or only application engineer on a data team — comfort owning the full lifecycle
Salary Range for this role: $185,000 \- $200,000
DDN has a very strong orientation towards these 4 characteristics and any successful employee will demonstrate these capabilities: Self\-Starter \- Takes independent action to identify and solve problems. Seeks out relevant information needed to make decisions. Gets involved with new initiatives.Success/Achievement Orientation \- Delivers quality results consistently. Targets, achieves (or exceeds) measurable results. Sets challenging goals, focuses on critical priorities, and is accountable.Problem Solving \- Recognizes problems and responds with a systematic assessment that identifies and addresses cause of issue. Practical, realistic, and resourceful.Innovative \- Builds and improves key business processes that enhance the effectiveness of DDN. Generates new ideas, challenges the status quo, and solves problems creatively. DataDirect Networks, Inc. is an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity, gender expression, transgender, sex stereotyping, sexual orientation, national origin, disability, protected Veteran Status, or any other characteristic protected by applicable federal, state, or local law.
\#LI\-Remote
Salary Context
This $185K-$220K range is above the median 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 DDN, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($202K) sits 12% above the category median. Disclosed range: $185K to $220K.
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.
DDN AI Hiring
DDN has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span New York, NY, US, TX, US. Compensation range: $220K - $220K.
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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