• Build and implement ML applications in structural biology, particularly around fine-tuning and extending foundational models like OpenFold, Boltz-2 and ESMFold. • Design and implement model extensions for specific tasks such as protein complex and binding affinity prediction, including data distillation, benchmarking, and evaluation pipelines. • Work with our customers and academic partners to define data preprocessing, selection, and benchmarking strategies for novel training tasks involving protein structures, complexes, and multimodal biological data. • Carry out case-studies associated with the above, providing scientific and technical expertise to our customers. • Be involved in the full project pipeline, from scoping through to results delivery and dissemination. • Design, build, and maintain scalable machine learning models and the pipelines needed for training, inference, and deployment in production. • Collaborate cross-functionally to ensure models address real-world drug discovery needs. • Contribute to publications or open-source contributions where relevant.

• Design, train, and deploy computer vision models to production with well-understood performance, latency, and cost characteristics. • Own the full ML pipeline: data preprocessing, feature engineering, model selection, training, evaluation, and deployment into sustainable inference services. • Conduct discovery spikes to validate feasibility and inform go/no-go decisions before committing to full development. • Integrate ML solutions with observability tooling, establishing and maintaining benchmarks to measure improvement and compare approaches. • Build automated, self-sustaining ML pipelines. Models should train, evaluate, and deploy with minimal manual intervention. • Inform build-vs-buy decisions with both technical rigor and business context, understanding when in-house models create competitive advantage vs. when vendor APIs are sufficient. • Collaborate with software engineers, data engineers, and product stakeholders to integrate ML solutions into CompanyCam's platform. • Communicate clearly with non-technical audiences about feasibility, requirements, and trade-offs of proposed solutions.

Role Description As one of our AI ML Engineer’s, you'll be a key technical leader and thought leader, shaping our ML strategy and building intelligent, high-performance multi-agent systems that perceive, learn, and act in real time. What You'll Do - Define and drive the technical vision for ML solutions across products and platforms - Own the end-to-end software development lifecycle — from design and code reviews through to deployment and operations - Architect high-performance, scalable microservices, including synchronous and asynchronous web services - Build real-time inference pipelines for complex models using Triton, TensorRT, and mixed-precision computing - Mentor engineers, set technical direction, and foster a strong team culture - Champion engineering excellence, system resilience, and continuous operational improvement Qualifications - Degree in Computer Science, Mathematics, or a related field - 5–10 years of full software development lifecycle experience (design, coding, testing, deployment, and operations) - 5–10 years of Python expertise, including advanced features and libraries - Strong experience designing RESTful APIs (e.g., FastAPI) - Proficiency with Docker, Kubernetes, and CI/CD pipelines - 3+ years designing and architecting large-scale distributed systems on cloud platforms (AWS, Azure, or GCP) - Proven experience as a tech lead or engineering mentor Requirements - MS or PhD in Computer Science or ML - Hands-on experience with ML frameworks: PyTorch, SageMaker, Triton, or TensorRT - Familiarity with NoSQL and document databases - Track record of handling big data, optimising workflows, and improving system performance Benefits - Health Care Plan (Medical, Dental & Vision) - Life Insurance (Basic, Voluntary & AD&D) - Paid Time Off (Vacation, Sick & Public Holidays) - Family Leave (Maternity, Paternity) - Training & Development

• Responsible for building robust chat applications and other generative AI solutions. • Develop and maintain the infrastructure for deploying AI agents. • Implement security best practices and ensure compliance with data protection regulations. • Set up and optimize observability pipelines for generative AI.