Machine Learning Engineer – Contract

Job Description:

• Design, train, and deploy ML models for text classification, entity extraction, clustering, and narrative and topic detection
• Build and improve LLM-powered features, including prompt design, retrieval-augmented generation (RAG), and agentic workflows
• Develop embedding and semantic-search pipelines to group related content and surface emerging threats
• Productionize models as scalable, monitored services with clear latency, cost, and accuracy targets
• Build evaluation frameworks and datasets to measure model quality, bias, and drift over time
• Work with data engineering to improve ingestion, labeling, and feature pipelines for high-volume, noisy, multilingual text data
• Monitor and retrain models in production, and debug quality issues end to end
• Document approaches and share trade-offs clearly with technical and non-technical stakeholders
• Work closely with data engineers, backend engineers, and product stakeholders as an individual contributor
• Help build an AI-powered risk intelligence platform that enables enterprises to detect, assess, and respond to disinformation, reputational attacks, and security risks

Requirements:

• 4+ years of professional experience building and deploying machine learning models in production
• Strong Python skills and hands-on experience with PyTorch, TensorFlow, or Hugging Face Transformers
• Practical NLP experience, such as classification, named entity recognition (NER), topic modeling, or semantic similarity on real-world text data
• Hands-on experience building with LLMs (OpenAI, Anthropic, or open-source models), including prompt engineering and RAG
• Experience with embeddings and vector search, such as pgvector, Pinecone, OpenSearch, or FAISS
• Experience deploying and operating ML services on AWS, GCP, or Azure, using Docker and CI/CD
• Solid grasp of model evaluation, including metrics, test sets, error analysis, and production monitoring
• Strong communication skills and comfort working in a remote, cross-functional team
• Applicants must be authorized to work for any employer in the U.S.; visa sponsorship is unavailable
• Nice-to-have: experience with trust and safety, threat intelligence, disinformation research, or social media data
• Nice-to-have: experience building agentic AI systems or tool-using LLM workflows, such as LangChain, LlamaIndex, or custom frameworks
• Nice-to-have: experience with multilingual NLP
• Nice-to-have: familiarity with MLOps tooling such as MLflow, SageMaker, Vertex AI, or Weights & Biases
• Nice-to-have: experience with streaming or large-scale data processing, such as Kafka, Spark, or Airflow
• Nice-to-have: experience fine-tuning LLMs with LoRA or PEFT or distilling models to reduce cost and latency

Benefits:

• Equal Opportunity Employer committed to fostering a diverse and inclusive workplace
• Application process accommodations available upon request