
Senior Manager, Software Engineering - Remote
- San Diego, CA
- $110,200-188,800 per year
- Permanent
- Full-time
- Architect, design and implement end-to-end ML/DL workflows: data ingestion, feature engineering, model training, evaluation, deployment and monitoring
- Lead proof-of-concept experiments in generative AI (transformers, state space models, LLMs) to solve business problems
- Define cloud-native ML infrastructure on Azure, AWS or GCP: containerization (Docker/Kubernetes), ML pipelines (SageMaker, Vertex AI, Azure ML), MLOps (CI/CD, model registry, monitoring)
- Establish best practices for model governance, versioning, reproducibility and security
- Collaborate with data engineers, data scientists, software engineers and product managers to translate business requirements into technical solutions
- Mentor and coach mid- to junior-level engineers and foster a culture of continuous learning
- Evaluate emerging tools, libraries and research to drive innovation and maintain competitive edge
- Document architecture designs, conduct design reviews and present technical proposals to stakeholders
- Collaborate with research, engineering, and product teams to translate cutting-edge AI advancements into production-ready capabilities
- Uphold ethical AI principles by embedding fairness, transparency, and accountability throughout the model development lifecycle
- Master’s or PhD in Computer Science, Statistics, Applied Mathematics, Electrical Engineering or related field
- 5+ years of professional experience in machine learning, data science or AI roles
- 4 + years of leadership experience
- Hands-on experience with Python and libraries: pandas, NumPy, scikit-learn
- Deep expertise in core ML and statistical methods: supervised/unsupervised learning, regression, classification, clustering, time series, Bayesian modeling
- Practical experience building or fine-tuning generative models (e.g., GPT, BERT, LLM)
- Demonstrated experience with cloud ML services and infrastructure design on at least one major cloud platform (AWS, Azure or GCP)
- Solid background in probability, linear algebra and statistical inference
- Proficiency in deep learning frameworks: TensorFlow, Keras, PyTorch
- Experience in the Healthcare domain: familiarity with clinical data standards (HL7, FHIR), regulatory requirements (HIPAA) and EHR systems
- Experience with MLOps tools: MLflow, Kubeflow, TFX, Airflow or equivalent
- Knowledge of data visualization tools (Tableau, Power BI) and dashboarding
- Familiarity with big data technologies: Apache Spark, Hadoop, Dask
- Contributions to open-source AI/ML projects or publications in peer-reviewed venues