Machine Learning Engineer | Contract | Remote
Machine Learning Engineer | Contract | Remote
Posted today
SENIORITY
Senior
About the role
- 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
- 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 RAGExperience 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/CDSolid 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
- 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 NLPFamiliarity 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
Before you apply
Applying takes about a minute. These four things decide how fast it moves after that.
Your profile is current
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Two examples you can talk through
Not a portfolio — just two pieces of work where you can explain the decisions and what you'd change.
A number in mind
What you're on now and what would make you move. We negotiate better when we know both.
Your notice period
Employers plan around it, and it's the question that stalls offers most often.
Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.
