Associate Principal Forensic AI Engineer Forensic Services practice

Charles River Associates

Location not specified

Full Time

Posted March 4, 2026

$140k - $170k

Not Specified

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Job Description

The role involves leading forensic investigations and developing AI-based solutions related to synthetic media, deepfake detection, and AI-generated content, while advising clients and legal teams on AI forensics, content authentication, and regulatory compliance.

Key Responsibilities

Lead and support forensic investigations involving AI-generated content, deepfakes, and AI-enabled fraud.
Develop, deploy, and validate AI-based detection tools and pipelines for media and text evidence analysis.
Perform content attribution, provenance analysis, and assess AI system vulnerabilities in investigations.
Design and build forensic data pipelines and tooling for large and varied datasets.
Advise legal counsel and executives on complex AI, media authenticity, and data integrity issues, translating technical findings into reports and testimony.
Support the development of AI solutions and lead technical vision for forensic projects.
Conduct structured and unstructured data analysis to support investigations and regulatory matters.

Requirements

Bachelor's degree required in Computer Science, Electrical Engineering, Data Science, Computational Linguistics, Information Systems, or a related technical field.
Graduate degree (M.S. or Ph.D.) in Machine Learning, Artificial Intelligence, Computer Vision, Natural Language Processing, or a closely related discipline preferred.
8 to 10 years of progressive experience in machine learning engineering, AI research, digital forensics, data science, or a closely related technical field.
Deep expertise in at least two of the following domains: development, fine-tuning, evaluation, or production deployment of large language models (LLMs) or generative AI systems; synthetic media detection, deepfake analysis, or AI-generated content forensics; natural language processing, computational linguistics, or authorship attribution; digital forensics, incident response, eDiscovery, or cybercrime investigation; consulting delivery, expert witness services, or client-facing technical advisory roles in litigation or regulatory contexts.
Experience in conducting or supporting expert witness engagements, producing legally defensible forensic reports, and communicating complex technical findings to non-technical audiences including judges, regulators, and corporate executives.
Proficiency with large language model architectures, including transformer-based models such as GPT, LLaMA, Mistral, Gemma, and related families, including experience with fine-tuning methodologies like LoRA, QLoRA, and instruction tuning, as well as alignment techniques such as RLHF, RLAIF, and DPO.
Experience with LLM ecosystems and tooling, including HuggingFace Transformers, PEFT, Datasets, Evaluate, TRL, inference frameworks like vLLM, llama.cpp, Ollama, and orchestration frameworks such as LangChain and LlamaIndex.
Experience building, evaluating, and optimizing Retrieval-Augmented Generation (RAG) pipelines using vector databases like Pinecone, Weaviate, ChromaDB, pgvector, and embedding models, including chunking strategies, retrieval evaluation, and hybrid search.
Competency in deepfake detection methodologies, including CNN- and transformer-based detection models trained on datasets such as FaceForensics and DFDC, and analysis of multimodal artifacts like lip synchronization, temporal consistency, and audio-visual misalignment, using tools such as Amped Authenticate and Sensity AI.
Understanding of content provenance standards, including C2PA Coalition for Content Provenance and Authenticity, cryptographic content credentials, digital watermarking approaches like SynthID, and blockchain-based authenticity verification.
Experience with NLP-based forensic methodologies, including stylometric analysis, semantic embedding-based authorship attribution, human vs. machine-generated text classification, and LLM source attribution, model fingerprinting, training data membership inference.
Advanced proficiency in Python, with competency in PyTorch and/or TensorFlow for deep learning model development and evaluation, as well as data analysis using pandas, NumPy, scikit-learn, and SQL for structured data querying and investigation support.
Experience with computer vision libraries such as OpenCV, torchvision, PIL/Pillow, and media forensics tools for metadata analysis, sensor pattern noise analysis, and compression artifact forensics.
Familiarity with cloud ML platforms including AWS SageMaker, GCP Vertex AI, Azure Machine Learning, and containerization tools like Docker and Kubernetes for deploying and managing AI forensic tooling in enterprise environments.
Experience with data engineering frameworks and tools such as Apache Spark, Airflow, and data warehousing platforms like Snowflake, BigQuery, and Redshift for processing large-scale unstructured and semi-structured datasets.