Machine Learning Engineer – Contract

This is a fully remote position, open to applicants in United States.

📋 Description

• Design, train, and implement machine learning models for tasks such as text classification, entity extraction, clustering, and detection of narratives and topics.

• Create and enhance features powered by large language models (LLMs), which include prompt design, retrieval-augmented generation (RAG), and workflows that are agentic in nature.

• Develop pipelines for embedding and semantic search to categorize related content and highlight emerging threats.

• Productionize models into scalable, monitored services with defined targets for latency, cost, and accuracy.

• Construct evaluation frameworks and datasets to assess model quality, bias, and drift over time.

• Collaborate with data engineering to enhance ingestion, labeling, and feature pipelines for handling high-volume, noisy, multilingual text data.

• Monitor and retrain production models while troubleshooting quality issues from start to finish.

• Document methodologies and communicate trade-offs clearly to both technical and non-technical stakeholders.

• Collaborate closely with data engineers, backend engineers, and product stakeholders as an individual contributor.

• Contribute to the development of an AI-driven risk intelligence platform that empowers enterprises to identify, evaluate, and react to disinformation, reputational threats, and security risks.

⛳️ Requirements

• A minimum of 4 years of professional experience in building and deploying machine learning models in production settings.

• Proficient in Python with hands-on experience using PyTorch, TensorFlow, or Hugging Face Transformers.

• Practical experience in natural language processing (NLP), including tasks like classification, named entity recognition (NER), topic modeling, or semantic similarity on real-world text data.

• Hands-on experience with large language models (LLMs) such as OpenAI, Anthropic, or open-source variants, including skills in prompt engineering and RAG.

• Experience with embeddings and vector search technologies like pgvector, Pinecone, OpenSearch, or FAISS.

• Proficient in deploying and managing ML services on cloud platforms such as AWS, GCP, or Azure, utilizing Docker and CI/CD practices.

• Solid understanding of model evaluation techniques, including metrics, test sets, error analysis, and production monitoring.

• Excellent communication skills and the ability to work effectively in a remote, cross-functional team environment.

• Applicants must have authorization to work for any employer in the U.S.; visa sponsorship is not available.

• Nice-to-have: experience in trust and safety, threat intelligence, disinformation research, or analysis of social media data.

• Nice-to-have: experience in developing agentic AI systems or workflows utilizing LLMs, such as LangChain, LlamaIndex, or custom frameworks.

• Nice-to-have: experience with multilingual NLP.

• Nice-to-have: familiarity with MLOps tools like MLflow, SageMaker, Vertex AI, or Weights & Biases.

• Nice-to-have: experience with streaming or large-scale data processing technologies such as Kafka, Spark, or Airflow.

• Nice-to-have: experience in fine-tuning LLMs using techniques like LoRA or PEFT, or in model distillation for cost and latency reduction.

🏝️ Benefits

• Equal Opportunity Employer dedicated to promoting a diverse and inclusive workplace.

• Application process accommodations available upon request.