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Machine Learning Engineer

About Lumino

At Lumino, our mission is to unlock the power of AI for every human, and we can’t do this without having the best people in the world on the team. AI is one of the next set of technologies that will unlock vast potential of human innovation, empowering us to solve problems that were thought to be unsolvable. Lumino is a technology company that builds infrastructure which enables anyone to create AI models. We are backed by prominent VCs such as Longhash Ventures, OP Crypto, Protocol Labs, Quaker Capital, Escape Velocity, and OrangeDAO.

We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status.

About the role:

We’re looking for a Machine Learning Engineer to join our team and help set the foundations of the company. You will be responsible for designing and building decentralized and distributed AI training pipelines, optimizing training for fast performance and low costs, and conducting research on cutting edge techniques for training on heterogeneous environments.

You will:

  • Design, develop, and optimize machine learning models and algorithms for various applications, including computer vision, NLP, and audio/video processing. Implement basic to advanced model architectures, starting with minimalist implementations.

  • Collect, clean, and preprocess data to create robust training datasets, working with complex datasets to ensure high-quality inputs for model training.

  • Train various deep learning models on different GPUs, including multi-GPU setups, and improve model performance and resource utilization by fine-tuning hyperparameters and using advanced techniques like LoRA and QLoRA quantization.

  • Evaluate model performance using appropriate metrics such as F1 scores, and conduct experiments to improve model accuracy and robustness.

  • Deploy machine learning models into production environments, ensuring scalability, efficiency, and reliability while managing the deployment of models on both cloud and bare-metal infrastructure.

  • Work closely with cross-functional teams to understand business requirements and translate them into technical solutions, collaborating with data scientists and software engineers to integrate models into production systems.

  • Monitor and maintain deployed models to ensure they continue to perform as expected, and implement processes for model retraining and updates as needed.

  • Optimize training performance on various GPUs (e.g., V100, T4, RTX3090, A100) and assess trade-offs to minimize training time and improve infrastructure efficiency.

  • Capture and analyze benchmarks on heterogeneous infrastructure, making performance improvements based on benchmark results.

  • Own and manage MLOps processes, train custom Lumino models including fraud detection, build and improve internal inference systems for model evaluation, and enhance existing evaluation processes in the ML pipeline.

Requirements:

  • You have 2+ years of experience as a Data Scientist or Machine Learning Engineer

  • You have 1+ years in Python and ML frameworks such as PyTorch, TensorFlow, and Jax.

  • You have experience in building, serving, and fine-tuning machine learning and large language models

  • You have strong analytical skills with the ability to navigate ML system trade-offs

  • You have a high degree of initiative and end-to-end project ownership

  • You have strong communication and collaboration abilities

  • You have excellent problem-solving skills and ability to learn quickly

Nice to haves:

  • Contributions to open source projects

  • Previous experience in a startup environment

  • Experience with latest Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) techniques.

What we offer:

  • Opportunity to design systems from the ground-up 🚀

  • Fast paced environment that allows you to learn and ship quickly 🛥️

  • Competitive salary + share of equity pool 💰

  • Medical, dental, and vision insurance 🏥

  • Whatever equipment you need to get the job done 💻

  • Github Co-Pilot and ChatGPT subscription ⚡

  • 3 days week in-office with the team, 2 days a week WFH 🙂

Average salary estimate

$105000 / YEARLY (est.)
min
max
$90000K
$120000K

If an employer mentions a salary or salary range on their job, we display it as an "Employer Estimate". If a job has no salary data, Rise displays an estimate if available.

What You Should Know About Machine Learning Engineer, Lumino AI

At Lumino, we’re on a mission to unlock the power of AI for everyone, and we need a talented Machine Learning Engineer to join our vibrant team in San Mateo. As a Machine Learning Engineer at Lumino, you will play a pivotal role in shaping the future of decentralized AI. Your day-to-day responsibilities will include designing and building AI training pipelines that are both efficient and cost-effective. You will engage with cutting-edge technologies in areas like computer vision, NLP, and audio/video processing while collaborating with cross-functional teams to translate business needs into technical solutions. We value creativity and innovation, encouraging you to implement and optimize models from the ground up. Your expertise will guide us in collecting, cleaning, and preprocessing data to ensure top-notch training datasets, as well as deploying these models into scalable and reliable production environments. Moreover, you will have the exciting opportunity to work with advanced GPU setups and leverage MLOps processes for continuous improvement of our ML models. If you have a passion for machine learning, a growth mindset, and enjoy tackling complex challenges, we would love to have you on board at Lumino!

Frequently Asked Questions (FAQs) for Machine Learning Engineer Role at Lumino AI
What are the key responsibilities of a Machine Learning Engineer at Lumino?

As a Machine Learning Engineer at Lumino, your responsibilities would include designing, building, and optimizing machine learning models, developing AI training pipelines, managing model deployments, and conducting research on advanced techniques tailored for decentralized systems. You’ll also work closely with cross-functional teams to ensure that your ML solutions meet the company's business requirements.

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What qualifications are needed for the Machine Learning Engineer role at Lumino?

The ideal candidate for the Machine Learning Engineer position at Lumino should have at least 2 years of experience in machine learning or data science, along with proficiency in Python and frameworks like PyTorch and TensorFlow. Familiarity with building and fine-tuning models, strong analytical skills, and excellent communication abilities are also essential to succeed in this role.

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How does Lumino support its Machine Learning Engineers in their roles?

At Lumino, we aim to foster a fast-paced learning environment. Machine Learning Engineers have the opportunity to design systems from scratch, have access to the latest tools and technologies, and receive medical benefits, competitive salaries, and equity options. We believe in providing the resources needed to get the job done effectively.

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What type of team environment can a Machine Learning Engineer expect at Lumino?

A Machine Learning Engineer at Lumino can expect a collaborative and dynamic team environment. Our culture promotes teamwork and cross-functional collaboration, enabling you to work closely with data scientists and software engineers. In addition, we encourage innovation, allowing your ideas to flourish while you tackle challenging projects.

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What development and training opportunities does Lumino offer to its Machine Learning Engineers?

Lumino places a high value on professional development. As a Machine Learning Engineer, you will have access to a variety of resources, including training programs, subscriptions to tools like GitHub Co-Pilot and ChatGPT, and opportunities to contribute to open-source projects. This ensures you stay at the forefront of advancements in machine learning.

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Common Interview Questions for Machine Learning Engineer
Can you describe your experience with machine learning frameworks like PyTorch and TensorFlow?

When answering this question, highlight the specific projects you have worked on, the challenges you faced, and how you overcame them. Mention any specific techniques or algorithms you implemented and the impact your contributions had on the project's success.

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How do you approach cleaning and preprocessing data for your ML models?

Explain your systematic approach to data cleaning, including techniques you use for handling missing values, outliers, and data normalization. It's also beneficial to discuss any tools or libraries you frequently use during this process.

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What strategies do you use to optimize the performance of your machine learning models?

Outline your techniques for model optimization, such as hyperparameter tuning, cross-validation, and the use of regularization methods. Be prepared to provide examples of how these strategies improved model performance in your previous projects.

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Can you give an example of a project where you implemented a deep learning model?

Share a detailed description of a project, including the model architecture you used, the dataset it was trained on, and the results it produced. Highlight any technical challenges you encountered and how you resolved them to achieve success.

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How do you ensure the scalability and reliability of deployed machine learning models?

Discuss best practices for deploying models in production, including monitoring performance, implementing continuous integration, and retraining processes. Provide examples of how you have addressed scalability challenges in past experiences.

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What are your thoughts on working in a decentralized AI environment?

Share your views on the advantages and challenges of decentralized AI development. Highlight your eagerness to learn and adapt to the unique aspects of this environment, showcasing your technical knowledge and enthusiasm for innovation.

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How do you keep current with advancements and trends in machine learning?

Elaborate on the resources you use to stay updated, such as scientific publications, online courses, podcasts, and machine learning communities. Mention your proactive approach to learning and integrating new knowledge into your work.

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Describe your experience with MLOps processes.

Discuss your understanding of the MLOps framework, its importance in the machine learning lifecycle, and any hands-on experience you have managing model lifecycles, including deployment, monitoring, and retraining.

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Can you talk about a time when you had to collaborate with cross-functional teams?

Provide a specific instance that showcases your collaboration skills. Highlight the role you played in facilitating communication between teams, any difficulties faced, and how your teamwork led to successfully integrating ML models into business operations.

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What metrics do you find most useful for evaluating the performance of machine learning models?

Explain the metrics you prioritize based on the specific use case of the model. Discuss why you prefer certain metrics like accuracy, precision, recall, or F1 score, and how they inform your model improvement processes.

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Full-time, hybrid
DATE POSTED
December 4, 2024

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