• Translating Requirements: Interpret vague requirements and develop models to solve real-world problems. • Data Science: Conduct ML experiments using programming languages with machine learning libraries. • GenAI: Leverage generative AI to develop innovative solutions. • Optimisation: Optimise machine learning solutions for performance and scalability. • Custom Code: Implement tailored machine learning code to meet specific needs. • Data Engineering: Ensure efficient data flow between databases and backend systems. • MLOps: Automate ML workflows, focusing on testing, reproducibility, and feature/metadata storage. • ML Architecture Design: Create machine learning architectures using Google Cloud tools and services. • Engineering Software for Production: Build and deploy production-grade software for machine learning and data-driven solutions.

• Build and productionize LLM and NLP models across retrieval, summarization, classification, and generative tasks by developing optimized embedding pipelines, prompt strategies, and fine tuning methods while translating experimental prototypes into robust components that consistently perform under production conditions. • Improve model accuracy, relevance, and robustness through structured evaluation frameworks, systematic error analysis, and iterative experimentation that ensures predictable behavior over time. • Design and implement scalable ML services and inference pipelines in Python using modern ML frameworks, incorporating efficient serving strategies such as batching, caching, streaming, and performance tuned deployment patterns that meet latency, reliability, and throughput requirements. • Integrate models with retrieval systems, feature stores, and knowledge sources while applying strong engineering discipline in automated testing, observability, logging, and operational readiness to support durable, maintainable ML systems. • Contribute to core architectural decisions that balance modeling complexity with performance, maintainability, and long term extensibility. • Translate complex NLP and LLM product requirements into structured engineering plans with clear milestones while collaborating closely with Product, Engineering, and Applied Science partners to align expectations, remove constraints, and deliver measurable customer impact. • Participate in technical design reviews and champion improvements in ML engineering practices including deployment standards, model QA, code quality, and operational excellence while providing informal mentorship on modern NLP techniques and scalable model serving. • Demonstrate strong ownership and attention to detail by driving high quality delivery in a fast moving environment focused on reliability, clarity, and meaningful outcomes.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description Natera is seeking a Staff Machine Learning Scientist – Translational AI to provide technical leadership at the intersection of biomedical foundation models, computational biology, and clinical translation. This role is responsible for shaping how genomic, pathology and multimodal foundation models are applied to high-impact translational problems, including: - Target identification - Drug and biomarker discovery - Patient stratification - Therapeutic development As a Staff-level scientist, you will operate with broad technical autonomy, influencing modeling strategy across multiple initiatives while remaining hands-on in model development, experimentation, and interpretation. You will work closely with AI scientists, translational scientists, bioinformatics, clinical partners, and ML engineers to ensure foundation models deliver biologically grounded and clinically meaningful insights. Qualifications - PhD in Computational Biology, Bioinformatics, Computer Science, or a related quantitative field. - 5+ years of experience applying ML to biological, genomic, or clinical data, in the field of oncology, immunology, or translational medicine. - Deep experience with foundation models, representation learning, self-supervised learning, or deep sequence models. - Demonstrated ability to translate ML outputs into biological insight or clinical value, not just metrics. - Strong proficiency in PyTorch and modern ML tooling (e.g., HuggingFace transformers, PEFT, Captum, MLFow). - Track record of scientific and technical leadership through project ownership, mentorship, or cross-team influence. Requirements - Experience integrating genomics with imaging or clinical data in multimodal foundation models. - Experience with drug discovery, clinical trial data, real-world evidence, or regulatory-facing analyses. - Strong publication record in ML, computational biology, or translational research venues. Benefits - Comprehensive medical, dental, vision, life and disability plans for eligible employees and their dependents. - Free testing for Natera employees and their immediate families, in addition to fertility care benefits. - Pregnancy and baby bonding leave. - 401k benefits. - Commuter benefits. - Generous employee referral program. Company Description Natera™ is a global leader in cell-free DNA (cfDNA) testing, dedicated to oncology, women’s health, and organ health. Our aim is to make personalized genetic testing and diagnostics part of the standard of care to protect health and enable earlier and more targeted interventions that lead to longer, healthier lives. The Natera team consists of highly dedicated statisticians, geneticists, doctors, laboratory scientists, business professionals, software engineers and many other professionals from world-class institutions, who care deeply for our work and each other. When you join Natera, you’ll work hard and grow quickly. Working alongside the elite of the industry, you’ll be stretched and challenged, and take pride in being part of a company that is changing the landscape of genetic disease management.

• Lead the design, development, and deployment of scalable machine learning and AI systems for enterprise clients. • Work at the intersection of machine learning, distributed systems, and production software engineering. • Help transform cutting-edge ML research into robust, production-grade solutions. • Guide ML architecture decisions and mentor engineers. • Collaborate with cross-functional teams to deliver high-impact AI solutions across multiple industries.