Senior Technical Researcher/Engineer

Harmattan AI

ABOUT US Harmattan AI is a next-generation defense prime building autonomous and scalable defense systems. Following the close of a $200M Series B, valuing the company at $1.4 billion, we are expanding our teams and capabilities to deliver mission-critical systems to allied forces. Our work is guided by clear values: building technologies with real-world impact, pursuing excellence in everything we do, setting ambitious goals, and taking on the hardest technical challenges. We operate in a demanding environment where rigor, ownership, and execution are expected. ABOUT THE ROLE As a Machine Learning Engineer on our Foundational team in Paris, you will build the "brain" of our tactical robots. You will design and scale large-scale, multi-modal foundational models that learn robust representations of the battlefield using Self-Supervised Learning (SSL) from massive amounts of unlabelled Electro-Optical (EO) and Infrared (IR) data. Your work provides the critical foundational weights that our Edge AI team distills into hyper-accurate models running on tactical hardware.   RESPONSIBILITIES * Multi-Modal SSL Architecture Design: Design neural network architectures (Vision Transformers) and loss functions (Masked Autoencoders, Contrastive Learning) to jointly learn from paired and unpaired EO and IR data. * Distributed Training Infrastructure: Manage and optimise training pipelines across multi-node GPU clusters, handling mixed-precision training and data loading. * Representation Evaluation: Develop metrics and linear-probing benchmarks to prove the latent space captures useful semantic features before distillation. * Data Strategy: Audit existing EO/IR data lakes and implement cross-attention mechanisms to fuse diverse sensor features. * Cross-Functional Collaboration: Sync with Data Engineers on ingestion pipelines and collaborate with the Edge AI team to ensure high-performance model handoffs. Candidate Requirements * Educational Background: A PhD or a highly research-focused MS in Computer Science, Machine Learning, Computer Vision, or Applied Mathematics. * Proven Experience: Minimum of 5-6 years of experience for senior levels. Experience training and scaling deep learning vision models (ViTs, CNNs) from scratch in multi-GPU/multi-node environments. Successful application of novel SSL or multi-modal architectures (e.g., CLIP, MAE, DINO) to real-world, non-standard imaging data (IR, SAR, or hyperspectral). * Technical Proficiency: Hardcore PyTorch engineering skills combined with deep mathematical intuition for representation learning. Knowledge of system-level languages (C++, Rust, or Go) and resource optimisation for edge computing. * Complexity & Leadership: Ability to architect state machines for fault-tolerant data pipelines and mediate technical trade-offs between hardware and algorithm teams. * Commitment & Mindset: 100% dedication to Harmattan AI’s mission of providing an ethical defence edge to allied countries. A hybrid researcher-engineer mindset that treats data quality as seriously as algorithm design We look forward to hearing how you can help shape the future of autonomous defense systems at Harmattan AI.

Harmattan AI

ABOUT US Harmattan AI is a next-generation defense prime building autonomous and scalable defense systems. Following the close of a $200M Series B, valuing the company at $1.4 billion, we are expanding our teams and capabilities to deliver mission-critical systems to allied forces. Our work is guided by clear values: building technologies with real-world impact, pursuing excellence in everything we do, setting ambitious goals, and taking on the hardest technical challenges. We operate in a demanding environment where rigor, ownership, and execution are expected. ABOUT THE ROLE As a Machine Learning Engineer on our Foundational team in Paris, you will build the "brain" of our tactical robots. You will design and scale large-scale, multi-modal foundational models that learn robust representations of the battlefield using Self-Supervised Learning (SSL) from massive amounts of unlabelled Electro-Optical (EO) and Infrared (IR) data. Your work provides the critical foundational weights that our Edge AI team distills into hyper-accurate models running on tactical hardware.   RESPONSIBILITIES * Multi-Modal SSL Architecture Design: Design neural network architectures (Vision Transformers) and loss functions (Masked Autoencoders, Contrastive Learning) to jointly learn from paired and unpaired EO and IR data. * Distributed Training Infrastructure: Manage and optimise training pipelines across multi-node GPU clusters, handling mixed-precision training and data loading. * Representation Evaluation: Develop metrics and linear-probing benchmarks to prove the latent space captures useful semantic features before distillation. * Data Strategy: Audit existing EO/IR data lakes and implement cross-attention mechanisms to fuse diverse sensor features. * Cross-Functional Collaboration: Sync with Data Engineers on ingestion pipelines and collaborate with the Edge AI team to ensure high-performance model handoffs. Candidate Requirements * Educational Background: A PhD or a highly research-focused MS in Computer Science, Machine Learning, Computer Vision, or Applied Mathematics. * Proven Experience: Minimum of 5-6 years of experience for senior levels. Experience training and scaling deep learning vision models (ViTs, CNNs) from scratch in multi-GPU/multi-node environments. Successful application of novel SSL or multi-modal architectures (e.g., CLIP, MAE, DINO) to real-world, non-standard imaging data (IR, SAR, or hyperspectral). * Technical Proficiency: Hardcore PyTorch engineering skills combined with deep mathematical intuition for representation learning. Knowledge of system-level languages (C++, Rust, or Go) and resource optimisation for edge computing. * Complexity & Leadership: Ability to architect state machines for fault-tolerant data pipelines and mediate technical trade-offs between hardware and algorithm teams. * Commitment & Mindset: 100% dedication to Harmattan AI’s mission of providing an ethical defence edge to allied countries. A hybrid researcher-engineer mindset that treats data quality as seriously as algorithm design We look forward to hearing how you can help shape the future of autonomous defense systems at Harmattan AI.

Maze

SUMMARY OF THE ROLE: At Maze, we're building AI-powered vulnerability management at a moment when generative AI is fundamentally changing what's possible in cybersecurity. Our engineering team is small, fast, and technically elite — and we're hiring an Engineering Director who is the same. This is not a coordination role. It is a senior technical leadership role for someone who earns respect by being on the tools, thinks deeply about architecture, and happens to be exceptional at growing engineers and running a high-performance org. You'll work in close partnership with our CTO, Santiago, taking ownership of a growing set of teams and tech leads as we scale from 20 to 35+ engineers. Your success will be measured the same way everyone's is at Maze: by the customer value and revenue impact of what your teams ship — not by process compliance, headcount growth, or delivery cadence. You'll have genuine accountability over a portion of the engineering org, with tech leads reporting to you, and you'll be expected to know your teams' codebases well enough to make good decisions, spot problems early, and earn the trust of every engineer who works with you. The person we're looking for has probably been the most senior technical person in a fast-moving 10–20 person engineering team — a CTO, VP, or Head of Engineering at a startup — and is ready to bring that energy and credibility to a company with more firepower behind it. If you want to stay close to the code, grow exceptional engineers, and help shape an org that will define how AI-native security companies are built, this is that role. YOUR CONTRIBUTIONS TO OUR JOURNEY: * Own engineering leadership for a growing portion of our org: Take clear accountability for a set of small, high-output product teams (typically 3–5 engineers each), with tech leads reporting directly to you. Ensure every team has unambiguous priorities, strong support, and everything they need to move fast. * Stay close to the technical work: Engage directly with architecture decisions, code reviews, and technical discussions across your teams. We expect you to spend meaningful time understanding what each team is building — not as a gatekeeper, but as a trusted technical voice who can contribute, challenge, and improve. * Grow the engineering leaders of Maze's future: Take ownership of career development, performance management, and coaching for tech leads and senior engineers in your teams. Build the kind of trust with engineers that comes from genuinely knowing their work, their growth areas, and their ambitions — not from generic 1:1s. * Drive cross-team coordination without creating bureaucracy: Own the planning and coordination layer that keeps multiple small teams aligned and unblocked. Keep it lightweight, decision-focused, and in service of engineering velocity — not process for its own sake. * Lead technical hiring: Take ownership of engineering hiring within your area, from defining the bar and shaping interview processes to closing exceptional candidates. The quality of who we hire is one of the highest-leverage things either of us can do. * Build the org we need to scale: Work closely with Santiago to design team structures, identify emerging leaders from within, and evolve how we operate as the team doubles. We grow leaders from the inside where we can — you'll be central to identifying, developing, and empowering the next generation. * Maintain technical excellence as we grow: Partner with tech leads to uphold code quality, shared engineering practices, and high standards across the org — without letting process replace judgment. WHAT YOU NEED TO BE SUCCESSFUL: * Technical credibility that engineers will respect: A strong engineering background with the ability to meaningfully engage in architecture discussions, code reviews, and technical decisions. You don't need to be the best coder in the room, but you need to be in the room for the right reasons. * Experience as the most senior technical leader of a small, high-output team: You've been a CTO, VP Engineering, or Head of Engineering at a startup — or a tech lead in a similarly fast-moving environment — where you were accountable for both technical outcomes and the people delivering them. You know what it looks like to lead 10–20 engineers, not just manage them. * Genuine people leadership, not just org design: A proven track record of performance management, career development, and growing engineers into leaders. You've had hard conversations, made difficult calls, and built cultures where high performance and high support coexist. * The hands-on instinct: You're drawn to being close to the work. You'd rather understand a problem by reading the code than by reading a status update. You see "staying technical" as a feature of your leadership style, not a tension with it. * Comfort with small-team operating models: Experience working in environments where teams are lean, fast, and expected to figure things out — not environments where scale compensates for speed. You know how to get a lot done with a little. * Strong hiring instincts: You've been deeply involved in engineering hiring — defining the bar, building processes, closing candidates — and you have strong opinions about what great looks like. * A business owner's mindset: You measure yourself by customer outcomes and revenue impact. You push your teams to understand why they're building what they're building, not just how. * Nice to Haves: * Experience in cybersecurity, AI, or security tooling — or a genuine interest in the domain and willingness to go deep quickly. * Founder or early-stage startup experience, particularly having built an engineering org from a small base. * Familiarity with agentic AI systems, LLMs, or ML-adjacent engineering — not as a researcher, but as someone who's built or led teams building on top of these technologies. * Experience managing distributed teams. WHY JOIN US: * Ambitious challenge: We're using generative AI — LLMs and agents — to solve some of the most pressing problems in cybersecurity today. The engineering challenges are genuinely hard, the domain matters, and we're early enough that the architectural decisions you make will define the platform for years. * Expert team: We are a team of hands-on leaders with experience at Big Tech and high-growth scale-ups, including teams behind multiple acquisitions and an IPO. We hire for quality over speed and it shows. * Impactful work: Cybersecurity is a force for good. The products your teams build directly help security teams protect their organisations against real attacks. The mission isn't decorative. * Build an AI-native engineering org from the ground up: We're designing the team structure, culture, and ways of working with a blank sheet of paper, in an era where agentic coding tools are changing what small teams can accomplish. You'll shape that from the start. * Real ownership and a clear growth path: You'll have genuine accountability over a meaningful portion of the organisation from day one, with a direct partnership with Santiago and significant equity upside as we scale.