Staff Machine Learning Engineer, Aignostics

• Solid foundations: Advanced degree in a relevant field or extensive work experience
• Proven experience: 8+ years of industry experience, with at least 2 years as Staff Engineer or an equivalent role
• Excellence in execution: Proven track record of driving technical excellence and innovation
• Software architect: Solid background in data-intensive systems and software architecture, design patterns and clean coding. Expert Python programming and fluency in C/C++ or other low-level language(s). Experience with designing and implementing large-scale, distributed ML systems and platforms; proven track record of deploying ML models into production environments
• Machine Learning expertise: Strong knowledge of machine learning fundamentals, experience with deep learning frameworks (e.g. Pytorch and Tensorflow) and state-of-the-art techniques (e.g. generative models)
• Cloud native: Deep understanding of cloud technologies (e.g. GCP, AWS), containerization and orchestration (Kubernetes). Experience with workflow orchestration tools (e.g. Prefect, Kubeflow or Airflow) and distributed computing frameworks (e.g. Ray, Spark)
• MLOps advocate: Familiarity with MLOps practices and model lifecycle management, complex CI/CD pipelines, infrastructure as code, code, experiment and model versioning tools (e.g. Git, WandB). Experience with designing and implementing large-scale, distributed ML systems and platforms; proven track record of deploying ML models into production environments, including performance monitoring (e.g. detection of data drift)
• Well-rounded communicator: Excellent communication skills, able to articulate complex technical concepts to both technical and non-technical stakeholders
• Self-driven learner: Stays up-to-date with technology trends and latest advancements in AI and ML technologies, explores and evaluates new solutions and grows with our challenges

Desirable

• As a Staff Machine Learning Engineer at Aignostics, you will play a crucial role in shaping the technical direction and architecture of our ML platform and infrastructure
• You will work hand in hand with fellow engineering leads at Aignostics, as well as collaborators in academia and industry
• Your expertise will be instrumental in driving technical excellence across all ML engineering teams, ensuring we deliver high-quality, scalable solutions that transform complex biomedical data into actionable insights for cancer research and diagnostics
• You will report directly to the Head of ML Engineering
• This is a unique opportunity to join a fun, diverse and growing team of 100+ software engineers, applied scientists, biologists and pathologists to shape the next generation of cancer treatments
• You will be part of a dedicated community that works in an agile, supportive and interdisciplinary research environment where your results make a difference to patients
• In our established startup, you have the opportunity to grow personally and technically, take on significant responsibilities and benefit from a dynamic work environment
• Steer: Define and drive the technical architecture and system design principles for our AI platform and infrastructure. Work in close collaboration with engineering leads to build flexible frameworks and systems for model training, evaluation and inference across different pathology applications
• Consult: Guide the CTO office, product management and fellow engineering leads through complex decisions by providing expert consultation on feasibility, architecture, trade-offs and risk mitigation strategies, while ensuring alignment with our technical vision
• Coordinate: Foster technical alignment across teams by establishing shared architectural principles and best practices, facilitating cross-team design reviews to enable consistent decision-making across domains
• Lead: Champion technical excellence by leading strategic initiatives that modernize our architecture and reduce technical debt while measuring and improving our technical health metrics
• Educate: Elevate the technical capabilities of our engineering staff through structured mentoring, workshops and establishing comprehensive technical guidelines that enable teams to make better design decisions
• Advocate: Drive innovation by evaluating emerging technologies, leading proof-of-concept initiatives and building support for strategic technical investments that advance our engineering capabilities while ensuring measurable business value