California Jobs
Machine Learning Research Engineer (1 Year Fixed Term)
California Jobs, Palo Alto, California, United States, 94306
Overview
Machine Learning Research Engineer (1 Year Fixed Term) at Stanford University, Enigma Project, Department of Ophthalmology, School of Medicine. This role focuses on building and fine-tuning large-scale multimodal foundation models and training frontier models on neural data to relate sensory input to neuronal correlates of perception, action, cognition, and intelligence. Responsibilities
Implement and optimize the latest machine learning algorithms/models to train multimodal foundation models on neural data Develop and maintain scalable, efficient, and reproducible machine-learning pipelines Conduct large-scale ML experiments using the latest MLOps platforms Run large-scale distributed model training on high-performance computing clusters or cloud platforms Collaborate with machine learning researchers, data scientists, and systems engineers to ensure seamless integration of models and infrastructure Monitor and optimize model performance, resource utilization, and cost-effectiveness Stay up-to-date with the latest advancements in machine learning tools, frameworks, and methodologies Other duties may also be assigned What we offer
An environment in which to pursue fundamental research questions in AI and neuroscience A vibrant team of engineers and scientists in a project dedicated to one mission Access to unique datasets spanning artificial and biological neural networks State-of-the-art computing infrastructure Competitive salary and benefits package Collaborative environment at the intersection of multiple disciplines Location at Stanford University with access to its world-class research community Strong mentoring in career development Key qualifications
Master's degree in Computer Science or related field with 2+ years of relevant industry experience, OR Bachelor's degree with 4+ years of relevant industry experience 2+ years of practical experience in implementing and optimizing machine learning algorithms with distributed training using common libraries (e.g. Ray, DeepSpeed, HF Accelerate, FSDP) Strong programming skills in Python, with expertise in machine learning frameworks like TensorFlow or PyTorch Experience with orchestration platforms Experience with cloud computing platforms (e.g., AWS, GCP, Azure) and their machine learning services Familiarity with MLOps platforms (e.g. MLflow, Weights & Biases) Strong understanding of software engineering best practices, including version control, testing, and documentation Preferred qualifications
Familiarity with training, fine tuning, and quantization of LLMs or multimodal models using common techniques and frameworks (LoRA, PEFT, AWQ, GPTQ, or similar) Familiarity with modern big data tools and pipelines such as Apache Spark, Arrow, Airflow, Delta Lake, or similar Experience with AutoML and Neural Architecture Search (NAS) techniques Contributions to open-source machine learning projects or libraries Education & Experience (REQUIRED)
Bachelor's degree and three years of relevant experience, or combination of education and relevant experience. Knowledge, Skills and Abilities (REQUIRED)
Thorough knowledge of the principles of engineering and related natural sciences. Demonstrated project management experience. Working Conditions
• May be exposed to high voltage electricity, radiation or electromagnetic fields, lasers, noise > 80dB TWA, allergens/bi Hazards/Chemicals/Asbestos, confined spaces, working at heights
• May require travel. Physical Requirements
• Frequently grasp lightly/fine manipulation, perform desk-based computer tasks, lift/carry/push/pull objects that weigh up to 10 pounds. • Occasionally stand/walk, sit, twist/bend/stoop/squat, grasp forcefully. • Rarely kneel/crawl, climb, reach/work above shoulders, use a telephone, writing by hand, sort/file paperwork or parts, operate foot and/or hand controls, lift/carry/push/pull objects that weigh >40 pounds. Affirmative Action & EEO
Stanford is an equal employment opportunity and affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law. Compensation
The expected pay range for this position is $126,810 to $151,461 annually. Stanford University provides pay ranges representing its good faith estimate of what the university reasonably expects to pay for a position. The pay offered to a selected candidate will be determined based on factors such as the scope and responsibilities of the position, the qualifications of the selected candidate, departmental budget availability, internal equity, geographic location and external market pay for comparable jobs. Additional information
Stanford University is committed to providing reasonable accommodations to applicants and employees with disabilities. Applicants requiring accommodation for any part of the application or hiring process should contact Stanford University Human Resources. For details about rewards and benefits, see the Cardinal at Work website. School of Medicine, Stanford, California, United States We’re always looking for people who can bring new perspectives and life experiences to our team. Found the perfect role and ready to apply? Learn more on what to expect next.
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Machine Learning Research Engineer (1 Year Fixed Term) at Stanford University, Enigma Project, Department of Ophthalmology, School of Medicine. This role focuses on building and fine-tuning large-scale multimodal foundation models and training frontier models on neural data to relate sensory input to neuronal correlates of perception, action, cognition, and intelligence. Responsibilities
Implement and optimize the latest machine learning algorithms/models to train multimodal foundation models on neural data Develop and maintain scalable, efficient, and reproducible machine-learning pipelines Conduct large-scale ML experiments using the latest MLOps platforms Run large-scale distributed model training on high-performance computing clusters or cloud platforms Collaborate with machine learning researchers, data scientists, and systems engineers to ensure seamless integration of models and infrastructure Monitor and optimize model performance, resource utilization, and cost-effectiveness Stay up-to-date with the latest advancements in machine learning tools, frameworks, and methodologies Other duties may also be assigned What we offer
An environment in which to pursue fundamental research questions in AI and neuroscience A vibrant team of engineers and scientists in a project dedicated to one mission Access to unique datasets spanning artificial and biological neural networks State-of-the-art computing infrastructure Competitive salary and benefits package Collaborative environment at the intersection of multiple disciplines Location at Stanford University with access to its world-class research community Strong mentoring in career development Key qualifications
Master's degree in Computer Science or related field with 2+ years of relevant industry experience, OR Bachelor's degree with 4+ years of relevant industry experience 2+ years of practical experience in implementing and optimizing machine learning algorithms with distributed training using common libraries (e.g. Ray, DeepSpeed, HF Accelerate, FSDP) Strong programming skills in Python, with expertise in machine learning frameworks like TensorFlow or PyTorch Experience with orchestration platforms Experience with cloud computing platforms (e.g., AWS, GCP, Azure) and their machine learning services Familiarity with MLOps platforms (e.g. MLflow, Weights & Biases) Strong understanding of software engineering best practices, including version control, testing, and documentation Preferred qualifications
Familiarity with training, fine tuning, and quantization of LLMs or multimodal models using common techniques and frameworks (LoRA, PEFT, AWQ, GPTQ, or similar) Familiarity with modern big data tools and pipelines such as Apache Spark, Arrow, Airflow, Delta Lake, or similar Experience with AutoML and Neural Architecture Search (NAS) techniques Contributions to open-source machine learning projects or libraries Education & Experience (REQUIRED)
Bachelor's degree and three years of relevant experience, or combination of education and relevant experience. Knowledge, Skills and Abilities (REQUIRED)
Thorough knowledge of the principles of engineering and related natural sciences. Demonstrated project management experience. Working Conditions
• May be exposed to high voltage electricity, radiation or electromagnetic fields, lasers, noise > 80dB TWA, allergens/bi Hazards/Chemicals/Asbestos, confined spaces, working at heights
• May require travel. Physical Requirements
• Frequently grasp lightly/fine manipulation, perform desk-based computer tasks, lift/carry/push/pull objects that weigh up to 10 pounds. • Occasionally stand/walk, sit, twist/bend/stoop/squat, grasp forcefully. • Rarely kneel/crawl, climb, reach/work above shoulders, use a telephone, writing by hand, sort/file paperwork or parts, operate foot and/or hand controls, lift/carry/push/pull objects that weigh >40 pounds. Affirmative Action & EEO
Stanford is an equal employment opportunity and affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law. Compensation
The expected pay range for this position is $126,810 to $151,461 annually. Stanford University provides pay ranges representing its good faith estimate of what the university reasonably expects to pay for a position. The pay offered to a selected candidate will be determined based on factors such as the scope and responsibilities of the position, the qualifications of the selected candidate, departmental budget availability, internal equity, geographic location and external market pay for comparable jobs. Additional information
Stanford University is committed to providing reasonable accommodations to applicants and employees with disabilities. Applicants requiring accommodation for any part of the application or hiring process should contact Stanford University Human Resources. For details about rewards and benefits, see the Cardinal at Work website. School of Medicine, Stanford, California, United States We’re always looking for people who can bring new perspectives and life experiences to our team. Found the perfect role and ready to apply? Learn more on what to expect next.
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