Senior Machine Learning Operations Engineer
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100% REMOTE Senior ML Ops Engineer / Lead Machine Learning Engineer Needed for Growing Subsidiary of a Large Public Company!
This Jobot Job is hosted by Reed Kellick
Are you a fit? Easy Apply now by clicking the "Easy Apply" button and sending us your resume.
Salary $155,000 - $235,000 per year
A Bit About Us
We are a growing subsidiary of a large public company that is hiring a talented Senior MLOps Engineer / Lead ML Ops Engineer!
Why join us?
As a Lead ML Engineer / Senior Machine Learning Engineer in our company, we are able to offer
- A competitive base salary between $155k and $235k, depending on experience!
- $12-14k in stock!
- Bonus of 15-20%, depending on experience!
- Work from home / work remote 100%!
- 401k with dollar for dollar match, up to 6% of eligible earnings (base, bonus). Plus additional company contribution!
- Comprehensive medical, dental, vision and life insurance!
- 17 paid holidays per year, including 3 floating holidays!
- Annual Paid Time Off (PTO), with separate sick days!
- 12 weeks paid Parental Leave!
- Caregiver Leave!
- Adoption and Surrogacy Assistance Plan!
- Flexible workplace accommodation!
- Fun team/company events at Sports games, concerts, etc.!
- Tuition reimbursement!
- Ability to attend conferences!
- A MacBook Pro and accompanying hardware to do great work!
- A modern productivity toolset to get work done Slack, Miro, Loom, Lucid, Google Docs, Atlassian and more!
- Generous company discounts!
- Eligible for donation matching to over 1.5 million nonprofit organization!
As a Lead Machine Learning Operations Engineer / Lead MLOps Engineer on our team, we are looking for
- Completed BS, MS, or PhD in Computer Science, Mathematics, Statistics, Data Science, Engineering, Operations Research, or other quantitative field
- 5+ years of practical experience in building, evaluating, scaling, and deploying machine learning pipelines with Python, preferably within the AWS ecosystem
- Strong programming skills in Python and understanding of core computer science principles
- Experience with frameworks and libraries for machine learning & AI such as scikit-learn, HuggingFace, PyTorch, Tensorflow/Keras, MLlib, etc.
- Ability to design, train, and evaluate machine learning and AI models while adhering to best practices including model selection, validation, bias/variance tuning, performance assessment, sensitivity analysis, dimensionality reduction, etc.
- Experience with MLOps practices such as automated model deployment, model performance monitoring, data drift detection, etc.
- Experience with building batch and streaming pipelines using complex SQL, PySpark, Pandas, and similar frameworks
- Experience with orchestrating complex workflows and data pipelines using like Airflow or similar tools
- Experience with architecting solutions on AWS or equivalent public cloud platforms
- Experience with Git, CI/CD pipelines, Docker, Kubernetes
- Experience with developing data APIs, Microservices and event driven systems to integrate ML systems
- Ability to load test deployed models at scale to understand performance breakpoints
- Familiarity with Large Language Models (LLMs), other generative AI modalities, and how they are applied in production
- Experience in assessing and implementing new data tools to enhance the machine learning stack
- Knowledge in domains such as recommender systems, fraud detection, personalization, and marketing science
- Knowledge of data mesh concepts
- Experience with managing and architecting solutions on AWS
- Familiarity with Snowflake, Monte Carlo, RDS, DynamoDB, Kafka, Fivetran, dbt, Airflow, Docker, Kubernetes, EMR, Sagemaker, DataDog, PagerDuty, Atlan, Data Observability tools and Data Governance tools
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Seniority level
Not Applicable -
Employment type
Full-time -
Job function
Engineering and Information Technology -
Industries
IT Services and IT Consulting and Software Development
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