TrustPDF Security Platform | TeraSystemsAI Careers

Platform Overview

Enterprise Document Integrity

TrustPDF Security establishes immutable chains of custody for mission-critical documents through cryptographic proof, blockchain anchoring, and sophisticated AI-powered forensic analysis. Our multi-layer verification architecture detects pixel-level alterations, metadata tampering, generative AI forgeries, and sophisticated social engineering attacks with unprecedented accuracy.

Built for regulated industries requiring legal-grade document assurance, our platform integrates seamlessly with existing document management systems, contract workflows, and compliance frameworks. Every verification generates cryptographically provable audit trails, permanently recorded on distributed ledgers for regulatory scrutiny and legal discovery.

Cryptographic Verification

SHA-256 hashing with blockchain anchoring
Digital signature validation (X.509, PGP)
Timestamp authority integration (RFC 3161)
Zero-knowledge proof protocols
Immutable audit trails on Ethereum, Polygon

Forensic Analysis

Pixel-level artifact detection using CNNs
Metadata inconsistency analysis
Font fingerprinting and typography forensics
Compression artifact identification
Copy-move and splice detection algorithms

AI-Powered Fraud Detection

Generative AI deepfake detection (GANs, diffusion)
Semantic coherence verification
Style transfer attack detection
Multi-modal consistency checks
Adversarial robustness testing

Enterprise Integration

RESTful API with batch processing
DocuSign, Adobe Sign connectors
SharePoint, Box, Dropbox integration
SAML 2.0 SSO and RBAC
Webhook notifications and SLA monitoring

Compliance and Security

SOC2 Type II certified infrastructure
GDPR and CCPA compliant data handling
AES-256 encryption at rest and in transit
HIPAA-ready for healthcare document workflows
Annual third-party security audits

Legal-Grade Reporting

Court-admissible verification reports
Chain of custody documentation
Expert witness testimony support
Detailed forensic analysis exports
Long-term archival with blockchain proofs

Join the TrustPDF Security Team

Build next-generation document authentication infrastructure at the intersection of cryptography, blockchain, and AI forensics

Hiring planned → Coming soon

Principal Cryptography Engineer

Location: Philadelphia, PA or Remote

Compensation: $210,000 - $290,000 + equity

Type: Full-time

Lead the design and implementation of cryptographic systems securing document verification at enterprise scale. Architect zero-knowledge proof protocols, blockchain anchoring mechanisms, and post-quantum cryptographic primitives for legal-grade document authentication.

Core Responsibilities

Design and implement cryptographic protocols for document integrity verification, including hash chain construction, Merkle tree optimization, and blockchain anchoring strategies
Develop zero-knowledge proof systems enabling privacy-preserving document verification without revealing sensitive content or metadata
Architect post-quantum cryptographic primitives to future-proof document authentication against quantum computing threats
Lead security audits and formal verification of cryptographic implementations, collaborating with third-party auditors and academic researchers
Build high-performance cryptographic libraries in Rust and C++ with SIMD optimization for sub-millisecond verification latency
Design key management infrastructure including HSM integration, multi-signature schemes, and threshold cryptography for distributed trust
Mentor security engineering team on cryptographic best practices, threat modeling, and secure coding standards

Required Qualifications

PhD in Cryptography, Computer Science, Mathematics, or equivalent research experience with 6+ years of applied cryptographic engineering in production systems
Deep expertise in public-key cryptography (RSA, ECC, Ed25519), digital signatures, hash functions, and authenticated encryption schemes
Proven track record implementing cryptographic protocols in production at scale, with publications in peer-reviewed venues (IACR, CCS, Oakland)
Strong systems programming skills in Rust, C++, or Go with experience optimizing cryptographic operations for performance and security
Experience with blockchain technologies (Ethereum, Polygon, Hyperledger) and smart contract security
Knowledge of post-quantum cryptography (lattice-based, code-based, hash-based signatures) and NIST PQC standardization efforts
Familiarity with hardware security modules (HSMs), trusted execution environments (TEEs), and secure enclaves

Preferred Qualifications

First-author publications at top-tier security conferences (IEEE S&P, USENIX Security, NDSS, ACM CCS)
Experience with formal verification tools (Coq, Isabelle, Tamarin) for cryptographic protocol analysis
Contributions to open-source cryptographic libraries (libsodium, OpenSSL, BoringSSL, Ring)
Background in zero-knowledge proofs (zk-SNARKs, zk-STARKs, Bulletproofs) and privacy-preserving computation
Understanding of side-channel attacks and countermeasures (timing, power analysis, fault injection)
Track record advising startups or Fortune 500 companies on cryptographic architecture and security strategy

Why This Role Matters

Your cryptographic innovations will secure legal contracts, financial transactions, healthcare records, and government documents affecting millions of people globally. Work at the forefront of applied cryptography, solving novel challenges at the intersection of blockchain, privacy, and enterprise security.

Express Interest

Senior ML Research Scientist (Forensics)

Location: Philadelphia, PA or Remote

Compensation: $180,000 - $250,000 + equity

Type: Full-time

Develop state-of-the-art machine learning models for detecting document forgeries, deepfakes, and AI-generated content. Pioneer novel computer vision architectures identifying pixel-level artifacts, semantic inconsistencies, and adversarial manipulations in multi-modal documents.

Core Responsibilities

Design and train deep learning models for document forgery detection, including GANs for artifact detection, transformers for semantic analysis, and multi-modal fusion architectures
Develop specialized computer vision models detecting pixel-level manipulations, copy-move attacks, splicing, and generative AI forgeries in PDFs, images, and scanned documents
Build adversarial robustness testing frameworks evaluating model performance against sophisticated evasion attacks and adaptive adversaries
Conduct original research publishable at top-tier ML/CV conferences (NeurIPS, ICML, CVPR, ICCV) advancing state-of-the-art in digital forensics
Collaborate with cryptography team to integrate ML-based verification with blockchain proofs and cryptographic audit trails
Optimize models for production deployment achieving <500ms latency on CPU inference with TensorRT, ONNX Runtime, and quantization techniques
Create synthetic training datasets using generative models and data augmentation to improve model generalization and reduce false positives

Required Qualifications

PhD in Computer Science, Machine Learning, Computer Vision, or related field with 5+ years of applied ML research in forensics, security, or adversarial ML
Strong publication record in ML/CV conferences (NeurIPS, ICML, ICLR, CVPR, ECCV) or security venues (USENIX Security, IEEE S&P)
Deep expertise in computer vision architectures (CNNs, ViT, CLIP) and generative models (GANs, VAEs, diffusion models)
Proficiency in PyTorch and TensorFlow with experience training large-scale models on distributed GPU clusters
Understanding of digital forensics techniques including error level analysis, JPEG compression artifacts, and metadata analysis
Experience with adversarial machine learning, including white-box and black-box attacks, adversarial training, and certified defenses
Strong software engineering skills with ability to deploy research models to production environments

Preferred Qualifications

First-author publications specifically on deepfake detection, image forensics, or document authentication
Experience with explainable AI techniques (GradCAM, SHAP, attention visualization) for forensic analysis
Background in multi-modal learning combining vision, language, and metadata signals
Familiarity with legal standards for expert testimony and court-admissible evidence
Contributions to open-source ML libraries or computer vision frameworks
Track record collaborating with interdisciplinary teams including cryptographers, legal experts, and security engineers

Why This Role Matters

Your ML innovations will combat financial fraud, protect legal proceedings, and maintain document integrity in critical infrastructure. Publish groundbreaking research while deploying models defending against real-world threats at Fortune 500 scale.

Express Interest

Director of Product, Security Platform

Location: Philadelphia, PA

Compensation: $190,000 - $260,000 + equity

Type: Full-time

Own product strategy and roadmap for TrustPDF Security platform, driving adoption among Fortune 500 legal teams, financial institutions, and government agencies. Translate complex cryptographic and ML capabilities into customer value, managing P&L and go-to-market execution.

Core Responsibilities

Define and execute product strategy for TrustPDF Security, balancing technical innovation with market demands in legal, financial, and government sectors
Own P&L for Security platform including revenue targets, pricing strategy, cost management, and unit economics optimization
Lead customer discovery and requirements gathering with Fortune 500 legal departments, Big Four accounting firms, and federal agencies
Collaborate with engineering and research teams to translate cutting-edge cryptography and ML research into production features with measurable customer impact
Build and manage product roadmap prioritizing enterprise integration, compliance certifications, API enhancements, and forensic capabilities
Drive go-to-market execution partnering with sales, marketing, and customer success teams to achieve ARR and retention targets
Establish product metrics, instrumentation, and analytics frameworks measuring feature adoption, verification accuracy, customer satisfaction, and business outcomes
Represent TrustPDF at industry conferences, customer advisory boards, and standards organizations (NIST, ISO, legal tech consortiums)

Required Qualifications

8+ years of product management experience with 5+ years in B2B enterprise SaaS, security software, or legal technology
Proven track record owning P&L and driving $10M+ ARR growth in technical products serving legal, financial services, or government customers
Deep understanding of enterprise security, compliance frameworks (SOC2, ISO 27001, FedRAMP), and regulatory requirements
Technical fluency to collaborate with cryptographers and ML researchers, translating complex capabilities into customer value propositions
Experience launching and scaling API-first products with strong developer ecosystem and partner integrations
Strong quantitative skills with expertise in product analytics, A/B testing, pricing optimization, and cohort analysis
Executive presence and communication skills to engage C-level buyers, lead customer advisory boards, and present at industry conferences

Preferred Qualifications

MBA from top-tier program or equivalent strategic business experience
Background in legal technology, e-discovery, contract lifecycle management, or digital forensics
Experience navigating government procurement processes (GSA schedules, FedRAMP authorization, DOD contracts)
Technical degree in Computer Science, Engineering, or quantitative field with understanding of cryptography and machine learning
Track record building 0-to-1 products in regulated industries with stringent compliance and security requirements
Network in legal, compliance, or risk management communities (ACC, ACAMS, SCCE)

Why This Role Matters

Shape the future of document trust infrastructure protecting legal proceedings, financial transactions, and government operations. Build a category-defining product at the intersection of AI, blockchain, and enterprise security serving the world's most demanding customers.

Express Interest