Machine Learning Engineer I, Network

About the role

Handshake is seeking a talented Machine Learning Engineer to join the Network and Handshake AI Marketplace Relevance team. This role is integral to developing and refining AI-driven solutions that help connect students with career opportunities effectively. You will be responsible for building core machine learning systems that power features such as job search, personalized recommendations, and user engagement across the platform. The position offers an opportunity to work on impactful projects that directly influence the user experience and platform growth. As part of the team, you will collaborate closely with product managers, data scientists, and engineering teams to translate user needs into scalable ML solutions, ensuring high performance, reliability, and relevance of the platform's AI capabilities.

Key facts

What you'll do

• Develop, implement, and optimize machine learning models for various platform features including search, recommendations, notifications, and user understanding.
• Build scalable ML pipelines that support feature creation, model training, evaluation, and deployment processes.
• Work with large datasets to engineer features, train models, and analyze performance metrics to improve model accuracy and relevance.
• Deploy models into production environments, ensuring they operate efficiently with low latency and high reliability.
• Contribute to the design and implementation of retrieval, ranking, and personalization systems that enhance user engagement.
• Collaborate with cross-functional teams to understand product requirements and translate them into effective ML solutions.
• Monitor and maintain production models, implementing updates and improvements based on performance data.
• Conduct experiments to evaluate model effectiveness, using marketplace metrics and user feedback to guide development.
• Participate in code reviews, technical discussions, and team planning to uphold best practices in ML engineering.
• Help define team standards for model development, testing, deployment, and monitoring.
• Support the integration of ML models into the broader platform infrastructure, ensuring operation.
• Assist in troubleshooting issues related to model performance, latency, or scalability.
• Stay current with advances in machine learning, deep learning, NLP, and related fields to bring innovative solutions to the team.
• Document models, pipelines, and processes to facilitate knowledge sharing and reproducibility.
• Contribute to the development of internal tools and frameworks that streamline ML workflows.
• Engage in continuous learning and professional development to enhance technical skills and domain knowledge.
• Support efforts to improve platform relevance and user satisfaction through data-driven insights.
• Participate in team meetings, sprint planning, and retrospectives to ensure alignment and continuous improvement.
• Contribute to a collaborative team environment that values innovation, quality, and impact.

Requirements

• Minimum of 3 years of professional experience in machine learning, data science, software engineering, or a related field.
• Strong proficiency in Python, with experience using ML frameworks such as scikit-learn, PyTorch, or TensorFlow.
• Proven track record of building, evaluating, and deploying machine learning models in production environments.
• Experience working on recommendation systems, search algorithms, personalization, ranking, NLP, deep learning, or large language models (LLMs).
• Solid understanding of core machine learning concepts including classification, regression, ranking, feature engineering, and model evaluation techniques.
• Hands-on experience with data pipelines, experiment tracking, and model monitoring tools.
• Strong software engineering fundamentals, including writing reliable, maintainable, and scalable code.
• Ability to analyze complex problems, evaluate tradeoffs, and deliver effective solutions with support.
• Excellent collaboration skills, with experience working alongside engineers, data scientists, product managers, and other cross-functional partners.
• Familiarity with cloud platforms and deployment strategies is a plus.
• Ability to work in a fast-paced environment and manage multiple priorities effectively.
• Passion for leveraging machine learning to solve real-world problems and improve user experiences.
• Strong communication skills, capable of articulating technical concepts to non-technical stakeholders.
• A focus on results, quality, and continuous improvement.

Nice to have

• Experience with embedding-based retrieval, multi-stage ranking, graph-based models, or recommender systems.
• Familiarity with handling large-scale datasets or high-traffic cloud-based production systems.
• Knowledge of generative retrieval techniques, LLM evaluation, or post-training methods.
• Interest in explainable AI, fairness, and responsible machine learning practices.
• Contributions to open-source projects or research in relevant fields.
• Experience with model interpretability tools and techniques.
• Ability to develop and implement innovative solutions that push the boundaries of current ML capabilities.
• Strong interest in staying updated with the latest research and industry trends.
• Enthusiasm for sharing knowledge and mentoring junior team members.

Skills & tools

• Python programming language
• scikit-learn
• PyTorch
• TensorFlow
• ML frameworks and libraries
• Data pipelines and ETL processes
• Experiment tracking tools
• Model monitoring and evaluation tools
• Cloud platforms (e.g., AWS, GCP, Azure) experience is a plus
• Version control systems such as Git
• Containerization and orchestration tools (e.g., Docker, Kubernetes) are beneficial

Practical notes

This role is designed for full-time employment within the United States. The company offers a comprehensive benefits package, including equity, a 401(k) match, paid parental leave, fertility benefits, and medical, dental, and vision coverage. Employees also receive mental health support, a $500 wellness stipend, and a $2,000 learning stipend to support ongoing professional development. The company promotes a flexible PTO policy, with 15 holidays and two flex days to accommodate personal needs. Additional support includes internet and commuting allowances, as well as free lunch and gym access for employees working at the San Francisco office. The position emphasizes a collaborative environment that values innovation, quality, and impact, providing opportunities for growth and meaningful contributions to the platform's success.