Senior ML Engineer

Jobgether; United States; Remote; USD 180,000 - 240,000; Full time

Job summary

Jobgether is seeking an experienced ML Platform Engineer in the United States to lead platform engineering initiatives covering the full ML lifecycle. You will design, develop, deploy, and operate scalable ML infrastructure, including model serving, feature computation, and monitoring, while collaborating with Data Science, Product, Risk, Fraud, and Engineering teams.

The role emphasizes production ownership, strong software fundamentals, and the ability to drive end-to-end projects with a focus

Qualifications

• At least 5 years of production software experience with Python.
• Strong fundamentals: API design, distributed systems, testing, maintainable code.
• Experience designing and operating cloud-native services in production.
• Hands-on ML platform, model serving, data platforms, or MLOps.
• Familiarity with feature engineering, model governance, deployment, and monitoring.

Responsibilities

• Lead ML platform engineering initiatives: Design, develop, deploy, and operate complex platform capabilities.
• Build scalable ML infrastructure for model deployment, serving, feature computation, data validation, orchestration, monitoring, and production support.
• Improve platform reliability and performance through reusable components, standards, automation, and cost efficiency.
• Own technical projects end to end, translating business needs into technical designs and coordinating delivery.

Skills

Python programming

API design

Distributed systems

Cloud-native design

Communication & collaboration

Tools

Docker

Kubernetes

CI/CD

Git workflows

Production observability tools

SQL

Job description

Accountabilities

• Lead ML platform engineering initiatives: Design, develop, deploy, and operate complex platform capabilities that support the complete machine learning lifecycle, ensuring systems are reliable, scalable, and maintainable.
• Build scalable ML infrastructure: Develop robust solutions for model deployment and serving, feature computation, data validation, workflow orchestration, monitoring, and production support.
• Improve platform reliability and performance: Build reusable components, standards, and automation that strengthen system availability, scalability, observability, operational readiness, and cost efficiency.
• Own technical projects end to end: Translate business requirements into technical designs, evaluate implementation options, communicate trade-offs, coordinate delivery, measure outcomes, and ensure successful production launches.
• Drive production troubleshooting: Lead investigations into complex production incidents, identify root causes, implement durable solutions, and establish preventive measures to reduce recurring issues.
• Collaborate across technical and business teams: Partner with Data Science, Product, Risk, Fraud, and Engineering stakeholders to understand business needs and deliver effective platform solutions.
• Shape technical strategy and architecture: Contribute to platform architecture, establish engineering standards, evaluate emerging technologies, and influence long-term technical direction.
• Strengthen engineering quality: Conduct thoughtful code and design reviews, improve testing and development practices, and promote maintainable software design across the engineering organization.
• Mentor and support engineers: Share technical knowledge through mentoring, pairing, and constructive feedback, helping colleagues develop their skills and deliver high-quality solutions.
• Improve the ML lifecycle: Support reliable processes spanning feature engineering, model evaluation, deployment, serving, monitoring, and governance, ensuring machine learning systems operate effectively in production.
• Leverage modern engineering tools: Apply AI-assisted development tools and automation where appropriate to reduce repetitive work, accelerate problem-solving, and improve engineering productivity.
• Connect engineering to business outcomes: Ensure platform investments deliver measurable improvements in operational efficiency, developer experience, system reliability, and customer outcomes.

Requirements

• Professional software engineering experience: At least 5 years of experience building, deploying, and operating production software systems, with strong programming skills in Python.
• Strong software engineering fundamentals: Deep understanding of API design, distributed systems, service architecture, software design principles, testing strategies, and maintainable code development.
• Cloud-native and scalable systems: Demonstrated experience designing and operating reliable, scalable cloud-native services or data-intensive systems in production environments.
• ML platform and MLOps expertise: Practical experience with machine learning platforms, model serving, feature engineering, data platforms, MLOps, or closely related infrastructure systems.
• Understanding of the ML lifecycle: Familiarity with the processes involved in feature engineering, model training, evaluation, deployment, inference, monitoring, and governance.
• Infrastructure and deployment technologies: Experience with Docker, Kubernetes, CI/CD pipelines, Git-based workflows, and production observability tools.
• Data and database proficiency: Practical experience with SQL, databases, caching systems, and tracing data or requests across distributed services.
• Production ownership: Ability to investigate complex technical problems, make sound architectural decisions, manage risks and dependencies, and deliver reliable solutions with minimal implementation guidance.
• Technical leadership: Demonstrated ability to lead significant engineering initiatives, mentor colleagues, provide constructive feedback, and influence technical decisions across teams.
• Communication and collaboration: Strong communication skills, with the ability to explain technical trade-offs clearly, align stakeholders, and connect engineering priorities to business, product, and operational objectives.
• Adaptability and independent judgment: Comfort working with ambiguity, navigating incomplete information, evaluating alternatives, and taking ownership of decisions and outcomes.
• AI-assisted engineering mindset: Willingness to experiment with AI tools, identify practical applications, and combine automation with sound engineering judgment to improve productivity.

Additional qualifications that are advantageous:

• Experience with cloud and data technologies such as AWS, Databricks, MLflow, Amazon SageMaker, Spark/PySpark, Kafka, or other streaming systems.
• Experience developing feature platforms, model-serving infrastructure, workflow orchestration systems, or internal developer platforms.
• Familiarity with monitoring model quality, data quality, feature coverage, inference latency, system reliability, and infrastructure costs.
• Experience working with sensitive data in regulated industries, particularly financial services.
• Proficiency in Java or another backend programming language in addition to Python.
• Familiarity with AI-powered productivity tools such as ChatGPT or GitHub Copilot.