This is a Fully Remote Job
About Our Client:
The organization operates in the Internet of Things (IoT) sector, focusing on connected operations for industries reliant on physical operations such as agriculture, construction, field services, transportation, and manufacturing. It addresses challenges in these foundational economic sectors by providing a cloud platform that leverages IoT data to generate actionable insights, aiming to improve safety, efficiency, and sustainability at scale.
About the Opportunity:
The SENIOR MACHINE LEARNING ENGINEER will develop and maintain the cloud-based backend systems that transform trained machine learning models into scalable, real-time safety features for AI dash cameras. This role is critical in ensuring reliable, low-latency delivery of machine learning applications that impact driver safety and operational risk, contributing directly to the organization's mission to enhance physical operations globally.
Responsibilities:
- Own the production lifecycle of machine learning models from artifact to cloud deployment.
- Define and implement standards for model serving, evaluation, versioning, and monitoring.
- Build and maintain reliable, low-latency ML APIs for integration with cloud applications.
- Develop scalable data pipelines for continuous model iteration, backtesting, and evaluation.
- Optimize and serve model artifacts for platform-specific workloads.
- Manage large-scale processing of sensor and telematics data to support ML operations.
- Monitor model performance metrics and ensure predictable failure modes.
- Collaborate with firmware, platform teams, and product managers for system integration and product alignment.
Requirements:
- Minimum 6 years' experience as a machine learning engineer or related role with proven production model deployment.
- Proficiency in programming languages such as C++, Golang, Java, Python, or Scala.
- Experience with ML tools like Ray Serve, MLflow, Grafana, PyTorch, and Spark.
- Skilled in deploying and refining models based on customer feedback.
- Strong full-stack or backend development knowledge.
- Bachelor’s or Master’s degree in Computer Science or related quantitative field.
Preferred Qualifications:
- Ph.D. in a quantitative discipline.
- Experience with containerization (Docker, Kubernetes), CI/CD, and infrastructure-as-code.
- Experience deploying ML applications in cloud environments (AWS, GCP, Azure).
- Expertise in optimizing distributed GPU-based model training.
Pay Range and Compensation Package:
- Annual base salary range: $170,170—$286,000 USD.
- Eligibility for initial RSU grants with no vesting cliff and ongoing refresh opportunities based on performance.
Benefits & Perks:
- Medical insurance
- Vision insurance
- Dental insurance
- 401(k)
- Paid maternity leave
- Paid paternity leave
- Commuter benefits
- Student loan assistance
- Tuition assistance
- Disability insurance
Equal Opportunity Statement: Our client is an equal opportunity employer. They celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, or national origin.
Note:
TalentHop is not the Employer of Record (EOR) for this role. Our purpose in this opportunity is to connect exceptional candidates with leading employers. We help job seekers worldwide discover roles that match their goals and guide them to complete their full application directly through the hiring company's career page or ATS.