Lead Machine Learning Engineer – News

Jobtailor; California (MO); On-site; USD 180,000 - 240,000; Full time

Job summary

Jobtailor is hiring a senior software engineer to own end‑to‑end ML initiatives, from design to production. You’ll build data pipelines, feature libraries, and orchestration layers that power ML models and recommendations.

You will collaborate with product, editorial, and engineering teams to translate business needs into scalable technical solutions, while prioritizing high‑impact work and maintaining strong code quality, testing, and observability.

Qualifications

• Bachelor’s degree in computer science, information systems, statistics, math, or comparable field of study, and/or equivalent work experience.
• 7+ years of software engineering experience.
• 5+ years of hands‑on experience developing and deploying machine learning systems in production.
• Expertise in data science, deep learning algorithms, or statistical methods to solve real‑world engineering problems.
• Comfortable operating at all levels of the predictive stack, including data collection, data analysis, feature engineering, batch training and low‑latency online serving.
• Experience designing and developing backend microservices for large-scale distributed systems using REST.
• Experience with cloud infrastructure, preferably AWS (Step Functions, Lambda, Glue, SQS, SNS, Personalize).
• Familiarity with developing and deploying Spark and ML pipelines.
• Hands‑on experience with big data technologies such as Databricks, Kinesis, Kafka.
• Proven leadership, coaching, and mentoring skills, with the ability to inspire and empower a team towards achieving business goals.
• Experience with observability tools for metrics, logging, and monitoring such as Datadog.
• Experience working in Agile/Scrum development environments.
• Excellent communication skills and a commitment to collaboration in a fast‑paced, guest‑focused environment.

Responsibilities

• Own complex technical initiatives end-to-end, from technical design through production deployment and operational excellence.
• Design and develop infrastructure supporting the full cycle of machine learning, including data pipelines and workflow orchestration, data discovery and quality tools, and feature libraries.
• Drive data and ML‑driven solutions for diverse engineering use cases such as recommendation systems, object detection, autogenerated tagging solutions, RAGs.
• Partner with product, editorial, and engineering stakeholders to translate business requirements into robust technical solutions.
• Strategically prioritize initiatives and technical workstreams to deliver the highest‑impact and most time‑sensitive outcomes, while proactively identifying, communicating, and mitigating risks to ensure successful execution.
• Champion engineering best practices across code quality, testing, CI/CD, observability, and incident response.
• Mentor and coach engineers, fostering a culture of ownership, collaboration, and continuous improvement.
• Contribute to technical documentation and promote knowledge sharing across teams.

Skills

Software engineering

Machine learning

MLOps

AWS

Spark

Databricks

Kafka

Kinesis

REST

Leadership

Communication

Education

Bachelor’s degree in CS/IS/Statistics or equivalent

Tools

Databricks

Kinesis

Kafka

Spark

REST API

AWS

CI/CD

Job description

Responsibilities

• Own complex technical initiatives end-to-end, from technical design through production deployment and operational excellence
• Design and develop infrastructure supporting the full cycle of machine learning, including data pipelines and workflow orchestration, data discovery and quality tools, and feature libraries
• Drive data and ML-driven solutions for diverse engineering use cases such as recommendation systems, object detection, autogenerated tagging solutions, RAGs
• Partner with product, editorial, and engineering stakeholders to translate business requirements into robust technical solutions
• Strategically prioritize initiatives and technical workstreams to deliver the highest-impact and most time-sensitive outcomes, while proactively identifying, communicating, and mitigating risks to ensure successful execution
• Champion engineering best practices across code quality, testing, CI/CD, observability, and incident response
• Mentor and coach engineers, fostering a culture of ownership, collaboration, and continuous improvement
• Contribute to technical documentation and promote knowledge sharing across teams

Requirements

• Bachelor’s degree in computer science, Information Systems, Statistics, Math, or comparable field of study, and/or equivalent work experience
• 7+ years of software engineering experience
• 5+ years of hands‑on experience developing and deploying machine learning systems in production
• Expertise in data science, deep learning algorithms, or statistical methods to solve real‑world engineering problems
• Comfortable operating at all levels of the predictive stack, including data collection, data analysis, feature engineering, batch training and low‑latency online serving
• Experience designing and developing backend microservices for large-scale distributed systems using REST
• Experience with cloud infrastructure, preferably AWS (Step Functions, Lambda, Glue, SQS, SNS, Personalize)
• Familiarity with developing and deploying Spark and ML pipelines
• Hands‑on experience with big data technologies such as Databricks, Kinesis, Kafka
• Proven leadership, coaching, and mentoring skills, with the ability to inspire and empower a team towards achieving business goals
• Experience with observability tools for metrics, logging, and monitoring such as Datadog
• Experience working in Agile/Scrum development environments
• Excellent communication skills and a commitment to collaboration in a fast‑paced, guest‑focused environment.