Machine Learning Engineer | Contract | Remote

We’re looking for a hands-on Machine Learning Engineer, for a project based assignment, to help build an AI-powered risk intelligence platform. The platform helps enterprises detect, assess, and respond to emerging online threats, including disinformation, reputational attacks, and security risks. You’ll design, build, and deploy NLP and LLM-based models that process large volumes of data from mainstream and niche online sources. Your work will feed directly into the signals customers act on. This is an individual-contributor role working closely with data engineers, backend engineers, and product stakeholders.

Key Responsibilities:

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

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 (e.g., 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

Nice to have:

Experience with trust and safety, threat intelligence, disinformation research, or social media data

Experience building agentic AI systems or tool-using LLM workflows (e.g., LangChain, LlamaIndex, or custom frameworks)

Experience with multilingual NLP

Familiarity with MLOps tooling such as MLflow, SageMaker, Vertex AI, or Weights & Biases

Experience with streaming or large-scale data processing (e.g., Kafka, Spark, or Airflow)

Experience fine-tuning LLMs (LoRA or PEFT) or distilling models to cut cost and latency

Location:

Remote, USA

Employment type:

Contract

*Applicants must be authorized to work for ANY employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time

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