• You'll manage and scale paid campaigns across Meta, TikTok, and Google — and help expand into newer channels like Reddit, Snapchat, and Quora. • Day to day that means setting up and optimizing campaigns, translating performance data into creative briefs, keeping reporting accurate and up to date, and flagging what's working and what isn't. • If you study ads while scrolling, save hooks that catch your eye, and naturally think about why certain creatives convert — this role was built for you.

Role Description We are looking for a technical lead to own delivery of our large molecule AI model programs. This is a hands-on leadership role at the intersection of foundation models, structural biology, protein engineering, and federated learning. You will lead teams building and operationalizing large-scale ML systems for: - Antibody modeling - Co-folding - Developability prediction - Biologics discovery You will turn ambitious scientific goals into reliable model systems that can be evaluated, released, and used in real drug discovery workflows. Your responsibilities include: - Setting technical direction and driving execution - Challenging modeling decisions and turning ambiguity into executable plans - Managing risks and dependencies - Mentoring senior engineers and ML scientists - Diving into technical depth when needed We are looking for someone who has led demanding ML delivery before and knows how to move from research-led or open-source prototypes to robust model systems. Qualifications - PhD, MSc, or equivalent experience in a relevant field - 5+ years applying ML to complex scientific or biological problems - Experience in structural biology, antibody engineering, biologics discovery, developability prediction, binder prediction, or protein design - Hands-on experience with modern ML systems in Python and PyTorch - Experience with large-scale models such as OpenFold, AlphaFold, Boltz, ESM, or similar Requirements - MLOps or ML infrastructure experience, particularly with Kubernetes-based training, evaluation, or deployment workflows - Ability to define success criteria and validate model quality - Experience leading delivery of complex ML projects - Comfortable operating as a player-coach, mentoring while contributing directly to modeling, experimentation, or architecture - Ability to work effectively with product, research, leadership, customers, and scientific stakeholders Nice to have - Experience with federated learning, privacy-preserving ML, distributed training, or other multi-party training environments - Experience on production-grade model delivery in regulated, enterprise, pharmaceutical, biotech, or other high-trust environments - Publication record in top-tier ML, computational biology, or structural biology venues such as NeurIPS, ICML, ICLR, ISMB, RECOMB, or similar Benefits - Industry-competitive compensation, including early-stage virtual share options - Remote-first working – work where you work best - Wellbeing budget, mental health support, work-from-home budget, co-working stipend, and learning budget - Generous holiday allowance - Office Days at our Berlin HQ or a different European location (3x per year) - A high-calibre, execution-focused team with experience from leading organizations

Role Description We are looking for an Edge AI Engineer to design, optimize, and deploy machine learning models that run efficiently on resource-constrained edge devices, including mobile platforms, embedded systems, and specialized accelerators. The role requires deep expertise in model compression, quantization, and hardware-aware optimization, along with strong systems engineering skills to ship reliable AI capabilities outside the data center. The ideal candidate has shipped edge AI in production environments where compute, memory, energy, and connectivity constraints fundamentally shape the engineering trade-offs. Key Responsibilities - Design and implement edge AI solutions optimized for diverse hardware including mobile SoCs, NPUs, and embedded accelerators. - Apply quantization, pruning, distillation, and architectural optimization to fit models within edge constraints. - Tune model performance for latency, energy efficiency, and memory footprint on target hardware. - Build cross-platform inference runtimes leveraging frameworks such as TensorFlow Lite, ONNX Runtime, and Core ML. - Optimize models for specific accelerator backends including DSPs, NPUs, and mobile GPUs. - Implement on-device model update, versioning, and rollback workflows that allow safe staged rollouts to large device populations and rapid recovery if a model release behaves unexpectedly in the field. - Design hybrid edge-cloud architectures that gracefully degrade based on connectivity and device capability. - Build telemetry pipelines that respect privacy while enabling continuous improvement. - Collaborate with hardware, firmware, and product teams to align AI capabilities with device constraints. - Implement secure execution paths, model protection, and integrity verification on edge devices. - Develop benchmarking suites that characterize accuracy, latency, and energy trade-offs across devices. - Drive responsible AI considerations including on-device privacy and bias evaluation. - Maintain comprehensive, current technical documentation — including architecture diagrams, design decisions, configuration references, runbooks, and operational procedures. - Stay current with edge AI hardware and software developments, regularly review release notes and community discussions, and translate noteworthy advances into concrete recommendations and adoption proposals for the team. Qualifications - Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related field. - Six or more years of experience in ML engineering, with significant work on edge or mobile AI. - Strong proficiency in Python and C++. - Hands-on experience with model compression, quantization, and pruning techniques. - Experience with at least one major edge inference framework. - Solid understanding of mobile and embedded hardware architectures. - Experience deploying ML models to production on mobile or embedded platforms. - Strong performance engineering and profiling skills. - Familiarity with on-device privacy and security considerations. - Strong communication and cross-functional collaboration skills. Preferred Qualifications - Experience with custom NPU or DSP toolchains. - Familiarity with federated learning or on-device personalization. - Exposure to safety-critical or industrial edge deployments. - Open-source contributions to edge AI frameworks. - Experience optimizing LLMs for on-device inference. How to Apply Would you like to know more about this opportunity? For immediate consideration, please send your resume to [email protected] or contact us at (908) 650-6699. Learn more about Bright Vision Technologies at www.bvteck.com .

• Design, train, evaluate, and ship machine learning and deep learning models (classification, regression, ranking, vision, NLP, time series) against well-defined business and scientific problems. • Design and implement LLM-powered applications using major model providers (Claude/Anthropic, Azure OpenAI, OpenAI, or equivalents). • Build retrieval-augmented generation (RAG) systems, including chunking strategies, embeddings, vector store selection (e.g., Azure AI Search, pgvector), and re-ranking. • Develop agentic workflows with tool use and orchestration; implement guardrails, evaluation harnesses, and human-in-the-loop review where appropriate. • Apply prompt engineering, fine-tuning, and structured output techniques; measure quality with offline evals and online metrics. • Build and maintain robust data and feature pipelines on Azure; ensure data quality, lineage, and reproducibility. • Productionize models with sound MLOps practices: versioning, automated retraining, drift detection, and rollback strategies. • Use AI coding assistants (Claude Code, Cursor) effectively alongside traditional manual coding — choosing the right approach for each task and reviewing AI-generated code with rigor. • Manage source code in Git using a clean branching strategy and pull-request-based review. • Build and maintain CI/CD pipelines (e.g., Azure DevOps, GitHub Actions) for automated testing, packaging, and deployment of AI services. • Deploy and operate services on Azure using containers (Docker), orchestration (Kubernetes/AKS), and infrastructure-as-code (Terraform or Bicep). • Instrument AI systems with logging, tracing, and evaluation pipelines so that quality, latency, and cost can be observed and managed over time. • Partner with security and compliance to address data privacy, PHI/PII handling, prompt injection, and model risk in regulated contexts. • Work directly with product, scientific, and business stakeholders to scope problems, set realistic expectations, and choose the right level of AI sophistication for the job. • Mentor engineers on AI-assisted development practices and on the patterns and pitfalls of LLM-based systems. • Conduct rigorous design and code reviews; advocate for evaluation, safety, and observability as first-class concerns.