Interested in this AI/ML Engineer role at The Associated Press?
Apply Now →Skills & Technologies
About This Role
The Associated Press is an independent global news organization dedicated to factual reporting. Founded in 1846, AP today remains the most trusted source of fast, accurate, unbiased news in all formats and the essential provider of the technology and services vital to the news business. More than half the world's population sees AP journalism every day.
Why this role matters:
The ML Engineer is a new role within the AP Engineering organization, responsible for shaping how we build and scale machine learning systems at AP, helping to lay the foundation for our machine learning capabilities. The ML Engineer has hands\-on experience building and optimizing ML inference systems that run in production environments. This role will develop and tune pipelines that transform millions of photos, videos, and text documents into searchable representations using a combination of deep learning models (e.g., DistilBERT, SBERT, TransNetV2\) and external multimodal APIs. The ideal candidate has experience optimizing inference at scale, orchestrating ML workloads, and working with both PyTorch and TensorFlow in a cloud environment, focusing on model performance, integration patterns, and inference efficiency.
This is an individual contributing role who will report directly to our Director of Development, Enterprise Application Services.
What you will do:
- Design, build, and scale ML\-powered inference systems that process large volumes of text, image, and video data to power news\-based intelligence products.
- Productionize and optimize state of the art models and inference pipelines. These models include, but are not limited to:
- + DistilBERT for Named Entity Recognition (NER) over hundreds of thousands of search queries/day
- + TransNetV2 for video shot boundary detection at scale for archival video as well as real\-time
+ SBERT for embedding generation from textual descriptions
+ External multimodal APIs for image/video captioning
- Support hybrid search architectures by defining embedding/re\-ranking interfaces, evaluation metrics, and inference performance requirements; partner with search/platform engineers on index configuration, sharding, and cluster tuning.
- Design and implement scalable data processing pipelines across hybrid CPU/GPU environments to handle millions of media assets.
- Partner with MLOps and platform engineering to enable the deployment and operation of ML systems reliably, contributing to:
- + Distributed inference architectures
- + Cloud\-based execution (e.g., AWS EC2, Batch, Lambda, SageMaker)
- + Efficient resource utilization across workloads
- Optimize inference latency and throughput across distributed workloads using cloud\-based resources (AWS EC2, Batch, Lambda, SageMaker, etc.)
- Build resilient asynchronous processing systems for large\-scale workloads, ensuring:
- + Reliability (retries, fault tolerance)
- + Efficiency (caching, deduplication)
- + Observability (metrics, logging, traceability)
- Work closely with data scientists and product teams to iterate on models, improve performance, and deliver measurable impact in production.
Who you are:
- 8\+ years of experience building production ML inference systems.
- Demonstrated ownership of deep\-learning inference optimization in production (quantization, distillation, compilation, kernel/profile\-level performance work) for transformer NLP and/or CV models.
- Experience with both TensorFlow (SavedModel, tf.data, XLA, TFLite) and PyTorch (TorchScript, ONNX, FastAPI/TorchServe)
- Hands\-on experience optimizing inference pipelines on AWS infrastructure, ideally across different types of media assets.
- Experience with video frameworks/tools (e.g., FFmpeg), and working with large\-scale frame\-level inference.
- Demonstrated experience monitoring and debugging model latency, memory, and pipeline throughput.
- Experience with hybrid search architectures (BM25 \+ vector search \+ cross\-encoder reranking).
- Familiarity with OpenAI APIs or other foundation model providers.
- Familiarity with open source HuggingFace LLMs.
- Experience with data pipeline and workflow orchestration tools (e.g., Airflow)
Who This Role is Not For:
Candidates whose primary background is MLOps platform work (e.g., DAG orchestration, Terraform, Kubernetes administration, generic CI/CD pipelines) will not be a fit. We are looking for a senior level engineer who has experience profiling a transformer, rewriting its serving path for a 2–3x latency reduction, tuning an HNSW index, and can tell us which SageMaker instance type will hit our p95 target at the lowest cost.
Why join us:
- A mission\-driven, inclusive environment focused on both individual and collective success.
- Opportunities for professional development to help you reach your career goals.
- Access to tools, mentorship, and resources tailored to elevate your proficiency and contributions.
Salary \& Benefits:
The anticipated salary range for this position is $145,000 \- $180,000 based on a candidate’s skills, qualifications and location. The Associated Press offers comprehensive benefits, which include:
- Competitive medical, dental and vision coverage
- Retirement benefits
- Company paid life insurance
- Paid vacation and sick days
- Paid parental leave for any new parent
- Mental well\-being resources
AP seeks to build an inclusive organization grounded in respect for differences. We support all aspects of diversity and provide equal employment opportunities to all employees and applicants without regard to race, color, religion, sex, marital status, national origin, age, sexual orientation, gender identity, disability, status as a veteran, or other characteristic protected by law.
Salary Context
This $145K-$180K range is below 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 The Associated Press, 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 ($162K) sits 10% below the category median. Disclosed range: $145K to $180K.
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.
The Associated Press AI Hiring
The Associated Press has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $155K - $180K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.