About this role
<p><span style="font-size:12.0pt;font-family:arial, helvetica, sans-serif"><span style="color:#333333">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.</span></span></p><p> </p> <div> <div> <p><strong><span style="font-size:12.0pt"><span>Why this role matters:</span><span> </span> </span></strong><br><span style="font-size:12.0pt"><span>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.</span><span> </span></span></p> </div> <div> <p><span style="font-size:12.0pt"><span>This is an individual contributing role who will report directly to our Director of Development, Enterprise Application Services.</span><span> </span></span></p> </div> <div> <p> </p> <p><strong><span style="font-size:12.0pt"><span>What you will do:</span><span> </span><span> </span></span></strong></p> </div> <div> <ul style="list-style-type:disc"> <li style="font-size:12.0pt"> <p><span style="font-size:12.0pt"><span>Design, build, and scale ML-powered inference systems that process large volumes of text, image, and video data to power news-based intelligence products.</span><span> </span></span></p> </li> </ul> </div> <div> <ul style="list-style-type:disc"> <li style="font-size:12.0pt"> <p><span style="font-size:12.0pt"><span>Productionize and optimize state of the art models and inference pipelines. These models include, but are not limited to:</span><span> </span></span></p> </li> </ul> </div> <div> <ul> <li style="list-style-type:none"> <ul style="list-style-type:circle"> <li style="font-size:12.0pt"> <p><span style="font-size:12.0pt"><span>DistilBERT for Named Entity Recognition (NER) over hundreds of thousands of search queries/day</span><span> </span></span></p> </li> </ul> </li> </ul> </div> <div> <ul> <li style="list-style-type:none"> <ul style="list-style-type:circle"> <li style="font-size:12.0pt"> <p><span style="font-size:12.0pt"><span>TransNetV2 for video shot boundary detection at scale for archival video as well as real-time</span> </span></p> </li> <li style="font-size:12.0pt"> <p><span style="font-size:12.0pt"><span>SBERT for embedding generation from textual descriptions</span> </span></p> </li> <li style="font-size:12.0pt"> <p><span style="font-size:12.0pt"><span>External multimodal APIs for image/video captioning</span><span> </span></span></p> </li> </ul> </li> </ul> </div> <div> <ul style="list-style-type:disc"> <li style="font-size:12.0pt"> <p><span style="font-size:12.0pt"><span>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.</span><span> </span></span></p> </li> </ul> </div> <div> <ul style="list-style-type:disc"> <li style="font-size:12.0pt"> <p><span style="font-size:12.0pt"><span>Design and implement scalable data processing pipelines across hybrid CPU/GPU environments to handle millions of media assets.</span><span> </span></span></p> </li> </ul> </div> <div> <ul style="list-style-type:disc"> <li style="font-size:12.0pt"> <p><span style="font-size:12.0pt"><span>Partner with MLOps and platform engineering to enable the deployment and operation of ML systems reliably, contributing to:</span><span> </span></span></p> </li> </ul> </div> <div> <ul> <li style="list-style-type:none"> <ul style="list-style-type:circle"> <li style="font-size:12.0pt"> <p><span style="font-size:12.0pt"><span>Distributed inference architectures</span><span> </span></span></p> </li> </ul> </li> </ul> </div> <div> <ul> <li style="list-style-type:none"> <ul style="list-style-type:circle"> <li style="font-size:12.0pt"> <p><span style="font-size:12.0pt"><span>Cloud-based execution (e.g., AWS EC2, Batch, Lambda, SageMaker)</span><span> </span></span></p> </li> </ul> </li> </ul> </div> <div> <ul> <li style="list-style-type:none"> <ul style="list-style-type:circle"> <li style="font-size:12.0pt"> <p><span style="font-size:12.0pt"><span>Efficient resource utilization across workloads</span><span> </span></span></p> </li> </ul> </li> </ul> </div> <div> <ul style="list-style-type:disc"> <li style="font-size:12.0pt"> <p><span style="font-size:12.0pt"><span>Optimize inference latency and throughput across distributed workloads using cloud-based resources (AWS EC2, Batch, Lambda, SageMaker, etc.)</span><span> </span></span></p> </li> </ul> </div> </div> <div> <div> <ul style="list-style-type:disc"> <li style="font-size:12.0pt"> <p><span style="font-size:12.0pt"><span>Build resilient asynchronous processing systems for large-scale workloads, ensuring: </span><span> </span></span></p> </li> </ul> </div> <div> <ul> <li style="list-style-type:none"> <ul style="list-style-type:circle"> <li style="font-size:12.0pt"> <p><span style="font-size:12.0pt"><span>Reliability (retries, fault tolerance) </span><span> </span></span></p> </li> </ul> </li> </ul> </div> <div> <ul> <li style="list-style-type:none"> <ul style="list-style-type:circle"> <li style="font-size:12.0pt"> <p><span style="font-size:12.0pt"><span>Efficiency (caching, deduplication) </span><span> </span></span></p> </li> </ul> </li> </ul> </div> <div> <ul> <li style="list-style-type:none"> <ul style="list-style-type:circle"> <li style="font-size:12.0pt"> <p><span style="font-size:12.0pt"><span>Observability (metrics, logging, traceability) </span><span> </span></span></p> </li> </ul> </li> </ul> </div> <div> <ul style="list-style-type:disc"> <li style="font-size:12.0pt"> <p><span style="font-size:12.0pt"><span>Work closely with data scientists and product teams to iterate on models, improve performance, and deliver measurable impact in production.</span><span> </span></span></p> </li> </ul> </div> <div> <p><span style="font-size:12.0pt"> </span></p> </div> <div> <p><strong><span style="font-size:12.0pt"><span>Who you are:</span><span> </span></span></strong></p> </div> <div> <ul style="list-style-type:disc"> <li style="font-size:12.0pt"> <p><span style="font-size:12.0pt"><span>8+ years of experience building production ML inference systems.</span><span> </span></span></p> </li> </ul> </div> <div> <ul style="list-style-type:disc"> <li style="font-size:12.0pt"> <p><span style="font-size:12.0pt"><span>Demonstrated ownership of deep-learning inference optimization in production (quantization, distillation, compilation, kernel/profile-level performance work) for transformer NLP and/or CV models.</span><span> </span></span></p> </li> </ul> </div> <div> <ul style="list-style-type:disc"> <li style="font-size:12.0pt"> <p><span style="font-size:12.0pt"><span>Experience with both TensorFlow (SavedModel, tf.data, XLA, TFLite) and PyTorch (TorchScript, ONNX, FastAPI/TorchServe)</span><span> </span></span></p> </li> </ul> </div> <div> <ul style="list-style-type:disc"> <li style="font-size:12.0pt"> <p><span style="font-size:12.0pt"><span>Hands-on experience optimizing inference pipelines on AWS infrastructure, ideally across different types of media assets. </span><span> </span></span></p> </li> </ul> </div> <div> <ul style="list-style-type:disc"> <li style="font-size:12.0pt"> <p><span style="font-size:12.0pt"><span>Experience with video frameworks/tools (e.g., FFmpeg), and working with large-scale frame-level inference.</span><span> </span></span></p> </li> </ul> </div> <div> <ul style="list-style-type:disc"> <li style="font-size:12.0pt"> <p><span style="font-size:12.0pt"><span>Demonstrated experience monitoring and debugging model latency, memory, and pipeline throughput.</span><span> </span></span></p> </li> </ul> </div> <div> <ul style="list-style-type:disc"> <li style="font-size:12.0pt"> <p><span style="font-size:12.0pt"><span>Experience with hybrid search architectures (BM25 + vector search + cross-encoder reranking).</span><span> </span></span></p> </li> </ul> </div> <div> <ul style="list-style-type:disc"> <li style="font-size:12.0pt"> <p><span style="font-size:12.0pt"><span>Familiarity with OpenAI APIs or other foundation model providers.</span><span> </span></span></p> </li> </ul> </div> <div> <ul style="list-style-type:disc"> <li style="font-size:12.0pt"> <p><span style="font-size:12.0pt"><span>Familiarity with open source HuggingFace LLMs.</span><span> </span></span></p> </li> </ul> </div> <div> <ul style="list-style-type:disc"> <li style="font-size:12.0pt"> <p><span style="font-size:12.0pt"><span>Experience with data pipeline and workflow orchestration tools (e.g., Airflow)</span><span> </span></span></p> </li> </ul> </div> <div> <p><span style="font-size:12.0pt"> </span><br><span style="font-size:12.0pt"> </span></p> </div> <div> <p><span style="font-size:12.0pt"><span><strong>Who This Role is Not For:</strong> </span><span> </span></span></p> </div> <div> <p><span style="font-size:12.0pt"><span>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.</span><span> </span></span></p> </div> </div> <div> <div> <p><span style="font-size:12.0pt"> </span></p> </div> <div> <p><strong><span style="font-size:12.0pt"><span>Why join us:</span><span> </span></span></strong></p> </div> <div> <ul style="list-style-type:disc"> <li style="font-size:12.0pt"> <p><span style="font-size:12.0pt"><span>A mission-driven, inclusive environment focused on both individual and collective success. </span><span> </span></span></p> </li> </ul> </div> <div> <ul style="list-style-type:disc"> <li style="font-size:12.0pt"> <p><span style="font-size:12.0pt"><span>Opportunities for professional development to help you reach your career goals. </span><span> </span></span></p> </li> </ul> </div> <div> <ul style="list-style-type:disc"> <li style="font-size:12.0pt"> <p><span style="font-size:12.0pt"><span>Access to tools, mentorship, and resources tailored to elevate your proficiency and contributions. </span><span> </span></span></p> </li> </ul> </div> <div> <p><span style="font-size:12.0pt"><span> </span><span> </span></span></p> </div> <div> <p><span style="font-size:12.0pt"><strong><span>Salary & Benefits: </span><span> </span></strong></span></p> </div> <div> <p><span style="font-size:12.0pt"><span>The anticipated salary range for this position is <strong>$145,000 - $180,000</strong> based on a candidate’s skills, qualifications and location. The Associated Press offers comprehensive benefits, which include: </span><span> </span></span></p> </div> <div> <ul style="list-style-type:disc"> <li style="font-size:12.0pt"> <p><span style="font-size:12.0pt"><span>Competitive medical, dental and vision coverage </span><span> </span></span></p> </li> </ul> </div> <div> <ul style="list-style-type:disc"> <li style="font-size:12.0pt"> <p><span style="font-size:12.0pt"><span>Retirement benefits </span><span> </span></span></p> </li> </ul> </div> <div> <ul style="list-style-type:disc"> <li style="font-size:12.0pt"> <p><span style="font-size:12.0pt"><span>Company paid life insurance </span><span> </span></span></p> </li> </ul> </div> <div> <ul style="list-style-type:disc"> <li style="font-size:12.0pt"> <p><span style="font-size:12.0pt"><span>Paid vacation and sick days </span><span> </span></span></p> </li> </ul> </div> <div> <ul style="list-style-type:disc"> <li style="font-size:12.0pt"> <p><span style="font-size:12.0pt"><span>Paid parental leave for any new parent </span><span> </span></span></p> </li> </ul> </div> <div> <ul style="list-style-type:disc"> <li style="font-size:12.0pt"> <p><span style="font-size:12.0pt"><span>Mental well-being resources </span><span> </span></span></p> </li> </ul> </div> <div> <p><span style="font-size:12.0pt"><span> </span><span> </span></span></p> </div> <div> <p> </p> </div> <div> <p><span> </span><span> </span></p> </div> </div><p style="margin-bottom:11.0px"><span style="font-size:12.0pt;font-family:arial, helvetica, sans-serif">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.</span></p> <p> </p>