Senior Software Engineer, 1

About The Team:

People Inc. is looking for a Senior Software Engineer 1 to join our AI/ML Engineering Platform team. As part of the AI/ML Engineering Platform team, you'll be working on widely used components that help users find ways to consume content on our sites. This includes using technologies such as Vertex AI pipeline, KServe, Kafka, Elasticsearch and Vector Database to leverage the power of AI and ML use cases and build capabilities to recommend related articles, and much more!

As a Senior Software Engineer 1, you will collaborate with product owners, Data Science, Platform teams, project managers, and software engineers to create service applications and contribute to the technical roadmap.

About The Positions Contributions:

Accountabilities, Actions and Expected Measurable Results (70%)

• Understand how to design and build scalable distributed systems, backend platforms, compatible with AI/ML infrastructure for search, retrieval, ranking, recommendation, and personalization use cases.
• Design and build systems, manage scalable ML pipelines using Vertex AI Pipelines for training, evaluation and deployment to support ranking, retrieval, and recommendation personalization use cases.
• Develop and maintain data pipelines that support feature generation, model training, and analytics workflows. Own vector generation via Milvus, storage, and retrieval workflows.
• Implement model serving solutions using KServe and build APIs using FastAPI for low latency inference.
• Build observability and monitoring for models and pipelines. Track performance, drift, failures, and data quality issues.
• Collaborate with data scientists, product managers, and platform teams to define and deliver ML driven features.
• Investigate production issues across data pipelines, models, and services. Identify bottlenecks and improve reliability and performance.
• Create and maintain clear documentation for pipelines, models, APIs, and operational processes.
• Develop internal tools and dashboards to provide visibility into data processing and model behavior for stakeholders.
• Contribute to engineering standards, code quality, and best practices across Python-based services and ML systems.
• Stay current with ML infrastructure, MLOps practices, and relevant tools. Bring in improvements where they add clear value.

Collaborate with product, data science, and frontend teams to deliver high quality search and feed experiences (30%)

• Own production systems. Debug issues across indexing, retrieval, ranking, and serving layers.
• Create clear documentation for pipelines, models, APIs, and system design.
• Contribute to best practices for Python based ML systems, API design, and scalable infrastructure.
• Stay current with advancements in search, ranking, and recommendation systems. Apply them where they make practical impact.

Education:

The Role’s Minimum Qualifications and Job Requirements:

Bachelor’s degree in Computer Science, Engineering, or a related field.

Experience:

You have a strong foundation in modern backend and ML engineering practices and continue to learn and evolve. You bring:

• 6+ years of experience building scalable backend systems and services.
• 5+ years of experience developing software using object oriented languages, with strong proficiency in Python, Node.js, and TypeScript.
• Hands-on experience with ES for search, indexing, and relevance tuning.
• Experience with event-driven systems using Apache Kafka for real-time data pipelines and processing.
• Strong understanding of version control systems including Git and platforms like Bitbucket.
• Experience with observability and monitoring tools such as Grafana, Kibana, and APM.
• Familiarity with cloud platforms including AWS and GCP, along with containerization using Docker and orchestration with Kubernetes.
• Comfortable deploying, versioning, and monitoring models in production.
• Curiosity to learn new technologies, especially in AI, LLMs, and modern search and recommendation systems, with a focus on applying them in real production use cases.
• Experience designing and building data pipelines using Apache Beam and Apache Airflow for ingestion, transformation, and feature pipelines.
• Familiarity with experimentation and analytics tools such as Jupyter Notebook and Apache Spark to track and reproduce experiments.
• Strong experience designing and consuming RESTful and GraphQL APIs, including versioning, documentation, and security practices like OAuth and JWT.
• Good understanding of machine learning concepts including supervised learning, unsupervised learning, deep learning, and natural language processing, with practical application in ranking, retrieval, and personalization.
• Beginner level experience managing ML pipelines using Vertex AI Pipelines for training, evaluation, and deployment workflows.
• Ability to review code, provide clear feedback, and improve overall engineering quality.
• Strong communication skills. Able to explain technical concepts clearly to both technical and non-technical stakeholders.
• Solid problem-solving skills with a data-driven approach.

Specific Knowledge, Skills, Certifications And Abilities:

Core Tech Stack:

• Backend and API development using Python, FastAPI, Node.js, and TypeScript.
• Search and indexing using Elasticsearch for relevance, retrieval, and query optimization.
• Event-driven architecture and streaming using Apache Kafka.
• Vector search and embeddings infrastructure using vector databases such as Milvus or Pinecone.
• Cloud and infrastructure using Google Cloud Platform or Amazon Web Services with containerization via Docker and orchestration through Kubernetes.

% Travel Required (Approximate): 0 %

It is the policy of People Inc. to provide equal employment opportunity (EEO) to all persons regardless of age, color, national origin, citizenship status, physical or mental disability, race, religion, creed, gender, sex, sexual orientation, gender identity and/or expression, genetic information, marital status, status with regard to public assistance, veteran status, or any other characteristic protected by federal, state or local law. In addition, the Company will provide reasonable accommodations for qualified individuals with disabilities. Accommodation requests can be made by emailing hr@people.inc.

The Company participates in the federal E-Verify program to confirm the identity and employment authorization of all newly hired employees. For further information about the E-Verify program, please click here: https://www.e-verify.gov/employees

Pay Range:

Salary: Remote: $125,000.00 - $150,000.00

The pay range above represents the anticipated low and high end of the pay range for this position and may change in the future. Actual pay may vary and may be above or below the range based on various factors including but not limited to work location, experience, and performance. The range listed is just one component of People Inc's total compensation package for employees. Other compensation may include annual bonuses, and short- and long-term incentives. In addition, People Inc. provides to employees (and their eligible family members) a variety of benefits, including medical, dental, vision, prescription drug coverage, unlimited paid time off (PTO), adoption or surrogate assistance, donation matching, tuition reimbursement, basic life insurance, basic accidental death & dismemberment, supplemental life insurance, supplemental accident insurance, commuter benefits, short term and long term disability, health savings and flexible spending accounts, family care benefits, a generous 401K savings plan with a company match program, 10-12 paid holidays annually, and generous paid parental leave (birthing and non-birthing parents), all of which may vary depending on the specific nature of your employment with People Inc. and your work location. We also offer voluntary benefits such as pet insurance, accident, critical and hospital indemnity health insurance coverage, life and disability insurance.

#NMG#

Key skills/competency:

• Senior Software Engineer
• AI/ML Engineering Platform
• Distributed Systems
• ML Pipelines
• Data Pipelines
• Vector Databases
• Model Serving
• Observability
• Python
• Elasticsearch