Senior · Staff · Principal Machine Learning Engineer
lai
If you're starting your career and want to work on real machine learning problems at scale, this is a solid next step. You'll design and implement ML models that actually ship to production, not just sit in notebooks. The role spans the full lifecycle: from raw data to deployed systems that people use.
Your day-to-day involves building ML algorithms and deep learning models, preparing datasets for training, and working with software engineers to get your models into production systems. You'll also monitor how models perform once they're live, optimize them for speed and accuracy, and keep learning as the field moves fast. It's collaborative work—you're paired with data scientists, engineers, and stakeholders solving real problems together.
This fits you if you have solid programming skills, understand ML fundamentals, and can write clean code. A background in computer science, statistics, math, or physics helps. You should have a portfolio or projects showing you can train models and think through data problems. Curiosity about how ML actually works in production matters more than years of experience.
Apply through CareerJumpShip with your resume and a link to your work. LAI is actively building their team and open to recent grads who can demonstrate capability.
About this role
Senior / Staff / Principal Machine Learning Engineer Location: Onsite San Francisco (5 days onsite AND hybrid options) We have multiple startups interested in talent. Here is a generic summary. Instead of a perfect job description, we present talented individuals to companies and allow them to share how that talent fits in the organization. Key Responsibilities: - Model Development: Designing and implementing ML algorithms and models, including deep learning models. - Data Handling: Preprocessing, analyzing, and preparing large datasets for model training and evaluation. - System Integration: Collaborating with software engineers to integrate ML models into production systems. - Performance Optimization: Continuously improving and optimizing ML models for accuracy, efficiency, and scalability. - Monitoring and Maintenance: Monitoring model performance in production, troubleshooting issues, and ensuring model reliability. - Staying Updated: Keeping abreast of the latest advancements in ML, AI, and related technologies. - Collaboration: Working with data scientists, software engineers, and other stakeholders to deliver effective ML solutions. Essential Skills: - Programming Languages: Strong proficiency in Python, R, or other relevant languages. - ML Frameworks: Experience with frameworks like TensorFlow, PyTorch, or scikit-learn. - Data Science Fundamentals: Solid understanding of statistical analysis, data modeling, and machine learning algorithms. - Problem-Solving: Excellent analytical and problem-solving skills to address complex challenges. - Communication: Effective communication skills to convey technical information to both technical and non-technical audiences. - Collaboration: Ability to work effectively in a team environment. Education and Experience: - A bachelor's or master's degree in computer science, engineering, mathematics, statistics, or a related field is typically required. - Several years of experience in machine learning, data science, or software development is often preferred. Compensation: Market range and can include equity – details can be provided after the specific client is determined.
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