Machine Learning Engineer

Whythisrolematters:
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 andoptimizingML 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 experienceoptimizinginference at scale, orchestrating ML workloads, and working with bothPyTorchand TensorFlow in a cloud environment, focusing on model performance, integration patterns, and inference efficiency.

This is an individual contributing role who will reportdirectlyto our Directorof Development, Enterprise Application Services.

What you will do:

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  Design, build, and scale ML-powered inference systems that process large volumes of text, image, and video data to power news-based intelligence products.

  
  

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  Productionize andoptimizestate of the artmodels and inference pipelines.These models include, but are not limited to:

  
  

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    DistilBERTfor Named Entity Recognition (NER) over hundreds of thousands of search queries/day

    
    
  
  
  
  

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    TransNetV2 for video shot boundary detection at scale for archival video as well as real-time

    
    
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    SBERT for embedding generation from textual descriptions

    
    
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    External multimodal APIs for image/video captioning

    
    
  
  
  
  

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  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.

  
  

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  Design and implement scalable data processing pipelines across hybrid CPU/GPU environments to handle millions of media assets.

  
  

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  Partner withMLOpsand platform engineering to enable the deployment and operation of ML systems reliably, contributing to:

  
  

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    Distributed inference architectures

    
    
  
  
  
  

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    Cloud-based execution (e.g., AWS EC2, Batch, Lambda, SageMaker)

    
    
  
  
  
  

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    Efficient resourceutilizationacross workloads

    
    
  
  
  
  

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  Optimizeinference latency and throughput across distributed workloads using cloud-based resources (AWS EC2, Batch, Lambda, SageMaker, etc.)

  
  

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  Build resilient asynchronous processing systems for large-scale workloads, ensuring:

  
  

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    Reliability (retries, fault tolerance)

    
    
  
  
  
  

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    Efficiency (caching, deduplication)

    
    
  
  
  
  

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    Observability (metrics, logging, traceability)

    
    
  
  
  
  

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  Work closely with data scientists and product teams to iterate on models, improve performance, and deliver measurable impact in production.

  
  

Who you are:

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  8+ years of experience building production ML inference systems.

  
  

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  Demonstrated ownership of deep-learning inference optimization in production (quantization, distillation, compilation, kernel/profile-level performance work) for transformer NLP and/or CV models.

  
  

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  Experience with both TensorFlow (SavedModel,tf.data, XLA,TFLite) andPyTorch(TorchScript, ONNX,FastAPI/TorchServe)

  
  

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  Hands-on experienceoptimizinginference pipelines on AWS infrastructure, ideally acrossdifferent typesof media assets.

  
  

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  Experience with video frameworks/tools (e.g.,FFmpeg), andworking with large-scale frame-level inference.

  
  

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  Demonstrated experience monitoring and debugging model latency, memory, and pipeline throughput.

  
  

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  Experience with hybrid search architectures (BM25 + vector search + cross-encoder reranking).

  
  

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  Familiarity with OpenAI APIs or other foundation model providers.

  
  

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  Familiarity with open sourceHuggingFaceLLMs.

  
  

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  Experience with data pipeline and workflow orchestration tools (e.g., Airflow)

  
  

Who This Role is Not For:

Candidates whose primary background isMLOpsplatform work (e.g.,DAG orchestration, Terraform, Kubernetes administration, generic CI/CD pipelines) will not be a fit. Weare looking fora senior level engineer whohas experienceprofilinga transformer, rewritingits serving path for a 2-3x latency reduction, tuningan HNSW index, andcantellus which SageMaker instance type will hit our p95 target at the lowest cost.

Whyjoin us:

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  A mission-driven, inclusive environment focused on both individual and collective success.

  
  

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  Opportunities for professional development to help you reach your career goals.

  
  

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  Access to tools, mentorship, and resources tailored to elevate yourproficiencyand 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:

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  Competitive medical, dental and vision coverage

  
  

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  Retirement benefits

  
  

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  Company paid life insurance

  
  

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  Paid vacation and sick days

  
  

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  Paid parental leave for any new parent

  
  

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  Mentalwell-beingresources