Staff Machine Learning Engineer (MLOps)

black.ai; United States; Remote; USD 90,000 - 150,000; Full time

Job summary

An innovative company is seeking a Machine Learning Engineer specializing in MLOps to operationalize ML models and enhance their deployment processes. This role is crucial for developing scalable infrastructure, automating workflows, and ensuring robust monitoring of ML systems. You will collaborate with data scientists and engineers to create efficient feature management and deployment pipelines. Join a remote-first environment that values diverse perspectives and fosters personal growth while making a significant impact in the financial technology landscape. If you are passionate about machine learning and eager to drive change, this opportunity is perfect for you.

Benefits

Competitive salary

Generous equity package

Full healthcare benefits

Technology expense reimbursement

Virtual first environment

Qualifications

• 3+ years of experience in MLOps or ML Engineering.
• Strong expertise in cloud platforms for ML deployment.

Responsibilities

• Design and implement scalable MLOps infrastructure.
• Automate ML workflows with CI/CD pipelines.
• Monitor and debug production ML systems.

Skills

MLOps

Machine Learning Engineering

DevOps

Cloud Platforms (AWS or Azure)

CI/CD Pipelines

Model Monitoring Tools

Orchestration Frameworks (Airflow, Kubeflow)

Pyspark

Infrastructure-as-Code (Terraform, CloudFormation)

Tools

Kubernetes

Airflow

GitHub Actions

Jenkins

Evidently AI

MLflow

Databricks

Job description

EMPOWER OVERVIEW

Empower is shaking up an outdated financial system by providing real opportunity for our customers: the opportunity to get the cash they need, to access fair credit, and to change their financial story. Today, we’re helping millions of people find financial security through machine learning models that evaluate creditworthiness using a more inclusive lens and mobile-first products: Cash Advance, Thrive line of credit, and Petal credit cards. Tomorrow? Creating even more financial paths for our customers (and their wallets) to succeed.

This year, Empower ranked #65 on Inc. 5000’s Fastest-Growing Private Companies list — our third year in a row cracking the top 100 — and was named by Forbes as one of the 25 Next Billion-Dollar Startups for 2024. Empower was also featured by Forbes on America’s Best Startup Employers list in 2023, and our Thrive line of credit product was named by Fast Company as one of 2022’s Next Big Things in Tech.

Empower is backed by Sequoia Capital, Blisce, and Icon Ventures. Ready to grow your impact and accelerate your career? Take a look at our open roles — we can’t wait to meet you.

THE EMPOWER WAY

1. Great Expectations: We come up with bold, audacious goals for ourselves and go all out for impact.
2. Owner Mindset: We give every employee latitude to act independently, make smart choices, and move the business forward.
3. Spirited Debate: We love skeptics and seek counter opinions to challenge our personal assumptions and expand our view.
4. Customer Obsession: We listen to understand, empathize, and create a memorable, rewarding experience for our community.
5. Inclusive Collaboration: We believe diverse teams make the best decisions, and we strive to give diverse voices a seat at the table.
6. No Jerks Allowed: We value our relationships and take the time to build trust and connection and communicate respectfully.

WHAT EMPOWER OFFERS

• Competitive salary
• Generous equity package
• Full healthcare benefits
• Technology expense reimbursement
• Virtual first environment

The Role

As our first Machine Learning Engineer focusing on MLOps, you will play a pivotal role in operationalizing our ML models, ensuring they are scalable, reliable, and easy to monitor. You’ll work closely with our data scientists, server engineers, and infrastructure teams to build feature stores for model development, facilitate model refit/retrain iterations, enhance model deployment (CI/CD) pipeline, as well as strengthen model monitoring.

Empower is a remote-first company. We drive connectivity through regular company offsites. Travel for company offsites is expected at a minimum of 2 times a year.

Key Responsibilities

• Design and Implement Scalable MLOps Infrastructure: Develop and maintain the infrastructure required for the entire machine learning lifecycle, from data ingestion and model training to deployment and monitoring.
• Build and Maintain Feature Stores: Design and implement robust feature stores to ensure consistent and efficient feature management across all ML models.
• Automate ML Workflows: Develop and implement CI/CD pipelines for machine learning models, automating the deployment and monitoring process.
• Implement Resource Management and Orchestration: Utilize tools like Kubernetes, Airflow, or similar technologies to effectively manage and orchestrate ML resources.
• Monitor and Debug Production ML Systems: Establish comprehensive monitoring and alerting systems to ensure the health and performance of deployed ML models.
• Collaborate with Data Scientists and Server Engineers: Work closely with data scientists and server engineers to understand their infrastructure needs and provide effective solutions.

Candidate Qualifications

• 3+ years of experience in MLOps, ML Engineering, or DevOps for machine learning.
• Experience with machine learning algorithms and model development process.
• Strong expertise in cloud platforms (AWS or Azure) for ML deployment.
• Experience with CI/CD pipelines (e.g., GitHub Actions, Jenkins) for ML.
• Strong knowledge of model monitoring tools (e.g., Evidently AI, MLflow).
• Hands-on experience with orchestration frameworks (e.g., Airflow, Kubeflow).
• Familiarity with Pyspark and Databricks is a plus.
• Familiarity with infrastructure-as-code (Terraform, CloudFormation) is a plus.

At Empower, we hire for people that push themselves to understand others and seek out ways to challenge their personal assumptions. Our hope is that by fostering such an environment, we strengthen our business and relationships by putting people first. We are committed to building a diverse, inclusive, and equitable workspace where everyone (regardless of age, education, ethnicity, gender, sexual orientation, or any personal characteristics) feels like they belong. Even if your experience doesn’t exactly match up to our job description, you should feel empowered to apply regardless!