Associate Principal/Forensic AI Engineer (Forensic Services practice)

Charles River Associates • Washington, DC

6h • $140,000 - $170,000 • Hybrid

About The Position

CRA’s Forensic Services practice supports companies’ commitment to integrity by assisting them and their counsel in independently responding to allegations of fraud, waste, abuse, misconduct, and non-compliance. We are noted for deploying cross-trained teams of forensic professionals to assist our clients in gaining deeper insights and greater value more quickly. We provide technical development, expert, and forensic services as well as cybercrime investigation services.
As an Associate Principal, you will lead projects that sit at the intersection of artificial intelligence, full stack development, forensic investigation, synthetic media analysis, and litigation support. In this role, you will serve as a key subject matter expert, builder, and technical advisor across a portfolio of unique client problems , including matters involving large language model (LLM) misuse, AI-generated content authentication, deepfake detection, and AI-enabled fraud. You will lead, learn from, and work alongside a team of like-minded, supportive, and highly technical colleagues.
A day in the life consists of collaborating across client matters, supporting forensic investigations, advising upon AI governance and synthetic media standards, building and deploying detection tooling, conducting structured and unstructured data analysis, and staying current with rapidly evolving developments in the generative AI and AI forensics space.

Requirements

• Bachelor’s degree required; Computer Science, Electrical Engineering, Data Science, Computational Linguistics, Information Systems, or a related technical field.
• 8–10+ years of progressive experience in machine learning engineering, AI research, digital forensics, data science, or a closely related technical field, with 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 a litigation or regulatory context
• A representative portfolio of project contributions , including open-source contributions, published research, technical blog posts, or other observable works , demonstrating sustained, applied AI proficiency across investigation-relevant domains.
• Demonstrated ability to conduct or support expert witness engagements, produce legally defensible forensic reports, and communicate complex technical findings to non-technical audiences including judges, regulators, and corporate executives.
• Familiarity with AI governance and risk management frameworks, including the NIST AI Risk Management Framework, ISO/IEC 42001:2023 (AI Management Systems), and the OWASP Generative AI Security guidelines.
• LLM Proficiency: Deep understanding of large language model architectures (transformer-based models including GPT, LLaMA, Mistral, Gemma, and related families); proficiency with fine-tuning methodologies including LoRA, QLoRA, and instruction tuning; and experience with alignment techniques (RLHF, RLAIF, DPO).
• LLM Ecosystems and Tooling: Proficiency with the HuggingFace ecosystem (Transformers, PEFT, Datasets, Evaluate, TRL); major LLM inference frameworks (vLLM, llama.cpp, Ollama); and orchestration frameworks including LangChain and LlamaIndex.
• Retrieval-Augmented Generation (RAG) Pipelines: Experience building, evaluating, and optimizing RAG pipelines using vector databases (Pinecone, Weaviate, ChromaDB, pgvector) and embedding models, including chunking strategy, retrieval evaluation, and hybrid search.
• Deepfake Detection and Synthetic Media Forensics: Competency in deepfake detection methodologies including CNN- and transformer-based detection models (trained on FaceForensics++, DFDC, or equivalent datasets); GAN architecture analysis; multimodal artifact inspection (lip synchronization, temporal consistency, audio-visual misalignment); and pixel-level manipulation detection tools (e.g., Amped Authenticate, Sensity AI).
• Content Provenance and Authentication: Understanding of content provenance standards including C2PA (Coalition for Content Provenance and Authenticity), cryptographic content credentials, digital watermarking approaches (including SynthID), and blockchain-based authenticity verification.
• Text Forensics and Authorship Attribution: 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).
• Programming Languages and Core Libraries: Advanced proficiency in Python; competency with PyTorch and/or TensorFlow for deep learning model development and evaluation; data analysis using pandas, NumPy, and scikit-learn; and SQL for structured data querying and investigation support.
• Computer Vision and Multimodal Analysis: Experience with computer vision libraries (OpenCV, torchvision, PIL/Pillow) and audio/video processing tools for media forensics, including EXIF metadata analysis, sensor pattern noise analysis, and compression artifact forensics.
• Cloud and Infrastructure: Familiarity with cloud ML platforms (AWS SageMaker, GCP Vertex AI, Azure Machine Learning) and containerization approaches (Docker, Kubernetes) for deploying and managing AI forensic tooling in enterprise environments.
• Data Engineering: Experience with data engineering frameworks and tools including Apache Spark, Airflow, and modern data warehousing platforms (Snowflake, BigQuery, Redshift) for processing and analyzing large-scale unstructured and semi-structured datasets.

Nice To Haves

• Graduate degree (M.S. or Ph.D.) in Machine Learning, Artificial Intelligence, Computer Vision, Natural Language Processing, or a closely related discipline preferred.

Responsibilities

• Support our dev team in building unique AI-based solutions for our clients.
• Lead and support technical vision and execution for forensic investigations involving AI-generated content, large language model misuse, deepfake media, synthetic voice and video, and AI-enabled fraud or misconduct.
• Develop, deploy, and validate deepfake detection pipelines and multimodal AI forensic tooling to analyze and authenticate text, image, audio, and video evidence at scale.
• Perform AI content attribution and provenance analysis, including authorship attribution for LLM-generated text, model fingerprinting, training data inference, and C2PA (Coalition for Content Provenance and Authenticity) manifest analysis.
• Apply LLM-based analytical frameworks, including retrieval-augmented generation (RAG) pipelines, structured output generation, and document intelligence, to accelerate investigation workflows and enhance analytical throughput.
• Conduct adversarial prompt analysis, prompt injection detection, and evaluation of AI system vulnerabilities in the context of client incidents, regulatory matters, and litigation.
• Design and build forensic data pipelines and investigatory tooling used to process and analyze large and varied datasets, including unstructured text corpora, media archives, model outputs, and system logs.
• Serve as technical subject matter expert advising legal counsel and corporate executives on complex AI, generative media, and data integrity challenges , translating sophisticated technical findings into defensible, plain-language expert reports and testimony suitable for judicial and regulatory audiences.
• Deliver training programs for clients and internal colleagues on responsible LLM use, AI-generated content identification methodologies, forensic readiness, and AI governance frameworks.
• Lead cross-functional engagements requiring coordination across technical analysis, legal strategy, digital forensics, and stakeholder communication under aggressive deadlines.
• Mentor junior team members.
• Contribute to internal initiatives, thought leadership, and practice development.

Benefits

• CRA’s robust skills development programs, including a commitment to offering 100 hours of training annually through formal and informal programs, encourage you to thrive as an individual and team member.
• We offer a comprehensive total rewards program including a superior benefits package, wellness programming to support physical, mental, emotional and financial well-being, and in-house immigration support for foreign nationals and international business travelers.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level