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
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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!
Job Details
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
Preferred Experiences & Skills
- 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
Interested in hearing more? Easy Apply now by clicking the "Apply Now" button.
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