Entrust
Role and Location Senior ML Engineer - Document Extraction Entrust (Identity Verification) Locations: London (Hybrid, 3 days onsite); Portugal (Hybrid or Remote) Department: Engineering Reports To: Engineering Manager Job Type: Full-Time The Role We are looking for a Senior Software Engineer to join the Document Fraud team, building products that support customers across our offerings, including financial services, driving verification, proof of address, and more. Our team develops fraud-detection capabilities that power identity, processing government identity documents to enable secure document verification, fraud prevention, and smooth customer onboarding experiences. As a Senior Software Engineer on the Document Extraction team, you will deliver high-accuracy extraction with global document coverage. You will contribute to our Document Verification offerings to help customers create secure onboarding experiences. To achieve this, you will leverage technology such as VLM/LLMs and traditional ML approaches in production systems at scale, tackling challenges from designing training and evaluation pipelines, optimizing inference for real-time extraction, and integrating as part of our product offering. The job is done when customers experience the benefits of modern technology. What You'll Do Work closely with Applied Science, Product, Design, Data Science, and Operations to deliver highly accurate and performant document classification and extraction solutions across thousands of documents globally. Lead the technical design of complex features and systems, owning RFC through implementation to production deployment. Produce clean, well-reasoned designs that avoid pitfalls and age well over time. Build and optimize production ML systems: develop repeatable pipelines for training, evaluating, and deploying LLM models; implement GPU optimizations for inference; design advanced labeling workflows to improve model accuracy; engineer robust solutions delivering measurable customer value through faster processing and more accurate extraction. Champion performance, scalability, and reliability by understanding how our systems operate in production; identify and tackle technical debt, scope and stage releases for smooth deployments, and build metrics to measure success empirically. Drive technical excellence by leading RFCs, reviewing critical code, ensuring quality through testing and documentation, and balancing priorities. Collaborate with Product to prioritize features and commitments. Mentor and enable other engineers through pair programming, technical guidance, and collaborative problem-solving to deliver quality code and grow team skills. Coordinate cross-cutting technical work across teams and org boundaries; track dependencies and assign clear owners to issues. Contribute to a culture of continuous improvement, psychological safety, and collaboration through squad-based organization, retrospectives, RFCs, DACIs, and cross-functional partnerships. What We're Looking For Strong production engineering experience: Built, deployed, and operated complex systems in production with emphasis on observability, reliability, performance, and scale. Hands-on LLM/ML systems experience: Production experience with LLMs, fine-tuning, inference pipelines, latency/cost optimization, or evaluation; familiarity with TensorFlow, PyTorch, or Triton; capable of productionizing ML. Technical depth and breadth: T-shaped with deep expertise in at least one area and broad software engineering knowledge. End-to-end ownership: Manage projects from idea to design, implementation, and production with minimal oversight. Tech stack: Python-based services (plus Ruby and TypeScript), deployed on AWS with Kubernetes; comfortable building production services in Python and understanding cloud infrastructure. Strong judgment and initiative: Make sound technical decisions in ambiguity, take initiative across multiple areas, and coordinate cross-team solutions. Mentorship and collaboration: Enable others, provide constructive code reviews, and foster team spirit. Pragmatic problem-solving: Identify and address technical debt, seek empirical validation, balance short-term needs with long-term architecture, and articulate scalability and reliability constraints. What We Offer Career Growth: We invest in your professional journey with learning-forward initiatives and challenging opportunities. Flexibility: Remote, hybrid, or on-site options to suit your lifestyle. Collaboration: Your voice matters and teams work together to build a better tomorrow. Diversity, Inclusion, and Accessibility Entrust prioritizes diversity, inclusion, and respect. We provide unconscious bias training for leaders and global affinity groups to connect colleagues worldwide. If you require an accommodation, contact . EEO/AA/Disabled/Veterans Employer Recruiter Jack Steib Entrust is an innovative leader in identity-centric security solutions with a global footprint and partner network, trusted by organizations worldwide.
Role and Location Senior ML Engineer - Document Extraction Entrust (Identity Verification) Locations: London (Hybrid, 3 days onsite); Portugal (Hybrid or Remote) Department: Engineering Reports To: Engineering Manager Job Type: Full-Time The Role We are looking for a Senior Software Engineer to join the Document Fraud team, building products that support customers across our offerings, including financial services, driving verification, proof of address, and more. Our team develops fraud-detection capabilities that power identity, processing government identity documents to enable secure document verification, fraud prevention, and smooth customer onboarding experiences. As a Senior Software Engineer on the Document Extraction team, you will deliver high-accuracy extraction with global document coverage. You will contribute to our Document Verification offerings to help customers create secure onboarding experiences. To achieve this, you will leverage technology such as VLM/LLMs and traditional ML approaches in production systems at scale, tackling challenges from designing training and evaluation pipelines, optimizing inference for real-time extraction, and integrating as part of our product offering. The job is done when customers experience the benefits of modern technology. What You'll Do Work closely with Applied Science, Product, Design, Data Science, and Operations to deliver highly accurate and performant document classification and extraction solutions across thousands of documents globally. Lead the technical design of complex features and systems, owning RFC through implementation to production deployment. Produce clean, well-reasoned designs that avoid pitfalls and age well over time. Build and optimize production ML systems: develop repeatable pipelines for training, evaluating, and deploying LLM models; implement GPU optimizations for inference; design advanced labeling workflows to improve model accuracy; engineer robust solutions delivering measurable customer value through faster processing and more accurate extraction. Champion performance, scalability, and reliability by understanding how our systems operate in production; identify and tackle technical debt, scope and stage releases for smooth deployments, and build metrics to measure success empirically. Drive technical excellence by leading RFCs, reviewing critical code, ensuring quality through testing and documentation, and balancing priorities. Collaborate with Product to prioritize features and commitments. Mentor and enable other engineers through pair programming, technical guidance, and collaborative problem-solving to deliver quality code and grow team skills. Coordinate cross-cutting technical work across teams and org boundaries; track dependencies and assign clear owners to issues. Contribute to a culture of continuous improvement, psychological safety, and collaboration through squad-based organization, retrospectives, RFCs, DACIs, and cross-functional partnerships. What We're Looking For Strong production engineering experience: Built, deployed, and operated complex systems in production with emphasis on observability, reliability, performance, and scale. Hands-on LLM/ML systems experience: Production experience with LLMs, fine-tuning, inference pipelines, latency/cost optimization, or evaluation; familiarity with TensorFlow, PyTorch, or Triton; capable of productionizing ML. Technical depth and breadth: T-shaped with deep expertise in at least one area and broad software engineering knowledge. End-to-end ownership: Manage projects from idea to design, implementation, and production with minimal oversight. Tech stack: Python-based services (plus Ruby and TypeScript), deployed on AWS with Kubernetes; comfortable building production services in Python and understanding cloud infrastructure. Strong judgment and initiative: Make sound technical decisions in ambiguity, take initiative across multiple areas, and coordinate cross-team solutions. Mentorship and collaboration: Enable others, provide constructive code reviews, and foster team spirit. Pragmatic problem-solving: Identify and address technical debt, seek empirical validation, balance short-term needs with long-term architecture, and articulate scalability and reliability constraints. What We Offer Career Growth: We invest in your professional journey with learning-forward initiatives and challenging opportunities. Flexibility: Remote, hybrid, or on-site options to suit your lifestyle. Collaboration: Your voice matters and teams work together to build a better tomorrow. Diversity, Inclusion, and Accessibility Entrust prioritizes diversity, inclusion, and respect. We provide unconscious bias training for leaders and global affinity groups to connect colleagues worldwide. If you require an accommodation, contact . EEO/AA/Disabled/Veterans Employer Recruiter Jack Steib Entrust is an innovative leader in identity-centric security solutions with a global footprint and partner network, trusted by organizations worldwide.
Entrust
Cambridge, Cambridgeshire
Location: Cambridge (Hybrid, 3 days onsite) Job type: Permanent, full-time Position Overview: Provides technical leadership in the design and delivery of FPGA-based solutions within complex, security-focused systems. Owns FPGA architecture, implementation, and integration, and contributes to system-level design and planning. Drives technical direction, delivery planning, and execution across FPGA workstreams, while remaining a hands-on contributor. Responsibilities: Leads the design, implementation, verification, and debug of VHDL-based FPGA solutions for new and existing products. Owns FPGA architectural design, including partitioning, interfaces, and performance trade-offs within system constraints. Drives integration of FPGA components with embedded and host software systems, ensuring robust and scalable solutions. Defines and structures FPGA development work, translating requirements into deliverable tasks and clear technical plans. Provides estimates for FPGA activities, ensuring plans are realistic, risks are understood, and dependencies are identified. Coordinates and balances FPGA work across engineers, aligning tasks to skill levels and supporting effective team delivery. Guides day-to-day execution, providing technical direction and unblocking engineers during implementation. Identifies and mitigates technical risks and delivery bottlenecks. Defines and evolves FPGA development processes, including configuration management, CI/CD, and verification practices. Contributes to system architecture, working with hardware, software, and product teams to define platform capabilities. Leads resolution of complex technical challenges (e.g. timing closure, resource optimisation, system integration). Mentors and develops engineers through technical guidance, design reviews, and knowledge sharing. Collaborates across teams to ensure alignment of FPGA deliverables with system and product objectives. Required Experience: Degree (or equivalent experience) in Electronic Engineering, Computer Science, Mathematics, or related discipline. Demonstrated experience designing and delivering complex FPGA-based systems, including: Synthesis, place & route, and timing closure Xilinx FPGA architectures and toolchains Experience translating algorithms into efficient FPGA implementations, optimised for performance, area, and power. Experience integrating FPGA designs into hardware/software systems, including bring-up and debugging. Software development experience (e.g. C or Python) to support tooling, test, or integration. Experience working in Linux and/or Windows development environments. Bonus Experience: Xilinx SoC platforms (e.g. Zynq, Versal) Intel (Altera) FPGA architectures, including SoCs Cryptographic algorithms or secure hardware systems Product compliance, certification, or regulated environments Personal Attributes: Demonstrates ownership and accountability for technical delivery. Applies a structured approach to planning, estimation, and execution. Effectively guides and develops other engineers, particularly less experienced team members. Maintains a hands-on approach while operating at system and team level. Strong collaborator, able to influence without authority. Comfortable working in complex, multi-disciplinary environments.
Location: Cambridge (Hybrid, 3 days onsite) Job type: Permanent, full-time Position Overview: Provides technical leadership in the design and delivery of FPGA-based solutions within complex, security-focused systems. Owns FPGA architecture, implementation, and integration, and contributes to system-level design and planning. Drives technical direction, delivery planning, and execution across FPGA workstreams, while remaining a hands-on contributor. Responsibilities: Leads the design, implementation, verification, and debug of VHDL-based FPGA solutions for new and existing products. Owns FPGA architectural design, including partitioning, interfaces, and performance trade-offs within system constraints. Drives integration of FPGA components with embedded and host software systems, ensuring robust and scalable solutions. Defines and structures FPGA development work, translating requirements into deliverable tasks and clear technical plans. Provides estimates for FPGA activities, ensuring plans are realistic, risks are understood, and dependencies are identified. Coordinates and balances FPGA work across engineers, aligning tasks to skill levels and supporting effective team delivery. Guides day-to-day execution, providing technical direction and unblocking engineers during implementation. Identifies and mitigates technical risks and delivery bottlenecks. Defines and evolves FPGA development processes, including configuration management, CI/CD, and verification practices. Contributes to system architecture, working with hardware, software, and product teams to define platform capabilities. Leads resolution of complex technical challenges (e.g. timing closure, resource optimisation, system integration). Mentors and develops engineers through technical guidance, design reviews, and knowledge sharing. Collaborates across teams to ensure alignment of FPGA deliverables with system and product objectives. Required Experience: Degree (or equivalent experience) in Electronic Engineering, Computer Science, Mathematics, or related discipline. Demonstrated experience designing and delivering complex FPGA-based systems, including: Synthesis, place & route, and timing closure Xilinx FPGA architectures and toolchains Experience translating algorithms into efficient FPGA implementations, optimised for performance, area, and power. Experience integrating FPGA designs into hardware/software systems, including bring-up and debugging. Software development experience (e.g. C or Python) to support tooling, test, or integration. Experience working in Linux and/or Windows development environments. Bonus Experience: Xilinx SoC platforms (e.g. Zynq, Versal) Intel (Altera) FPGA architectures, including SoCs Cryptographic algorithms or secure hardware systems Product compliance, certification, or regulated environments Personal Attributes: Demonstrates ownership and accountability for technical delivery. Applies a structured approach to planning, estimation, and execution. Effectively guides and develops other engineers, particularly less experienced team members. Maintains a hands-on approach while operating at system and team level. Strong collaborator, able to influence without authority. Comfortable working in complex, multi-disciplinary environments.
