Sr. Machine Learning Engineer
FrontApp
- San Francisco, CA
- Permanent
- Full-time
- Design and develop advanced Machine Learning (ML) models to solve challenging problems related to natural language processing (NLP), recommendation systems, and predictive analytics.
- Collaborate with product and engineering teams to understand business needs and identify opportunities for the application of ML and AI technologies.
- Lead the exploration of new ML techniques and technologies to enhance the capabilities of our products and improve the user experience.
- Pioneer the exploration and adoption of new Generative AI techniques
- Mentor junior engineers and ML specialists, fostering a culture of innovation and continuous learning within the team.
- Ensure the quality and availability of data for model training and evaluation.
- Continuously evaluate and improve the performance and scalability of ML models in production environments.
- Stay abreast of developments in the field of ML and AI, and advocate for the adoption of industry best practices and emerging technologies.
- 5+ years of experience in designing, implementing, and deploying ML models, with a strong portfolio of projects that demonstrate your expertise.
- Deep understanding of ML algorithms, data structures, and software engineering principles.
- Proficiency in programming languages such as Python and experience with ML frameworks like TensorFlow, PyTorch.
- Demonstrable experience driving impact by building production applications using ML models and learning based on production analytics and data.
- Practical experience with natural language processing (NLP) technologies, recommendation systems, predictive analytics, Generative AI.
- Strong experience with prompting techniques, RAG pipelines, evaluation mechanisms and fine-tuning SLMs, LLMs preferred.
- Strong problem-solving skills and the ability to work independently as well as in a team environment.
- Excellent communication skills, with the ability to convey complex technical concepts to non-technical stakeholders.
- Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related field.
- Experience with cloud computing platforms like AWS, Google Cloud Platform, or Azure, especially with services related to ML and data analytics.
- Familiarity with MLOps tools like Weights & Biases, MLFlow, Metaflow, Modal, etc.
- Familiarity with containerization and orchestration technologies such as Docker and Kubernetes, particularly in the context of deploying and scaling ML models.
- Advanced knowledge in specialized areas of ML such as deep learning, reinforcement learning, or unsupervised learning
- Practical experience with big data technologies such as Hadoop, Spark, or Kafka for processing and analyzing large datasets.
- Proficiency in additional programming languages (e.g., JavaScript, R) or experience with front-end technologies (e.g., JavaScript, React, Angular) for end-to-end ML system development.
- Published research papers in top ML or AI conferences and journals.