Senior Software Engineer 1, ML

Senior Software Engineer 1, ML

As a Senior Software Engineer for personalization, you will own the design, development, and continuous improvement of the recommendation algorithm that powers the user's personalized product feed. You'll work with a rich dataset of user-saved products and a live ingestion pipeline pulling from thousands of retailer feeds to build a system that learns each user's unique preferences across brand, category, color, price point, and fit.

This is a high-ownership, high-impact role. You will collaborate closely with product, engineering, and data teams to define what great personalization looks like — and then build it.

Remote or Hybrid 3x a month

In-office Expectations: This position offers remote work flexibility; however, if you reside within a commutable distance to one of our offices in New York, Des Moines, Birmingham, Los Angeles, Chicago, or Seattle, the expectation is to work from the office two days per week.

Our next-generation product discovery platform connects shoppers with the things they love across thousands of retail partners. Users save, organize, and share products they're excited about — and our platform turns those signals into a deeply personalized shopping experience. We ingest live product feeds from thousands of retailers and use a rich understanding of each user's taste to surface the right product at the right moment.

We're building the recommendation engine at the heart of this shopping experience — a system that understands not just what people save, but why they save it. This is a foundational hire that will shape how millions of users discover products they love.

Accountabilities, Actions and Expected Measurable Results

• Recommendation Algorithm Development 30%
• Design and build the core personalization engine using user-saved product data as behavioral signals.
• Develop multi-signal recommendation models that incorporate brand affinity, product category, color palette, fit/sizing signals, price sensitivity, and trends.
• Implement and evaluate a range of approaches including collaborative filtering, content-based filtering, and hybrid neural architectures.
• Build and maintain product embedding models that capture rich semantic similarity across the retailer feed catalog.
• Develop cold-start strategies to generate high-quality recommendations for new users with limited save history.
• Data Ingestion & Feature Engineering 25%
• Design and maintain robust pipelines to ingest, normalize, and enrich product feeds from thousands of retail partners.
• Collaborate on a unified product taxonomy and attribute extraction layer that standardizes inconsistent retailer data into coherent features (category, color, material, fit, etc.).
• Leverage NLP and computer vision techniques to extract attributes from unstructured product descriptions and images.
• Partner with the data engineering team to maintain data quality, freshness, and catalog coverage at scale.
• Personalized Feed & Ranking 25%
• Build and own the ranking and re-ranking layer that assembles each user's personalized feed in real time.
• Develop and tune multi-objective ranking that balances relevance, novelty, diversity, and business goals (e.g., promoted/sponsored retailer partnerships).
• Implement feedback loops that continuously update user preference models based on implicit signals (saves, clicks, dwell time, shares).
• Build A/B testing solutions to rigorously evaluate ranking and recommendation changes against key engagement metrics.
• Engineering Excellence 20%
• 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 ML systems, API design, and scalable infrastructure.
• Stay current with advancements in recommendation, ranking, and personalization systems and apply them where they make practical impact.

Education:

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:

• 5+ years of ML engineering experience focused on recommendation systems, personalization, or search ranking with hands-on depth in collaborative filtering, matrix factorization, content-based, and hybrid neural approaches.
• Proven experience designing, training, and deploying embedding models and vector retrieval (e.g., Milvus, Pinecone) for product or content similarity at catalog scale.
• Production experience serving real-time, low-latency ML predictions and managing the full model lifecycle — training, deployment, versioning, and monitoring — on cloud ML platforms such as AWS SageMaker or GCP Vertex AI (including Vertex AI Pipelines).
• Rigorous experimentation discipline: experiment design, A/B and multivariate testing, and the analytical ability to translate model results into clear product and business decisions.
• Extensive backend engineering with strong proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, or JAX), plus working knowledge of Node.js and TypeScript.
• Experience designing large-scale data and feature pipelines using Apache Kafka, Spark, Beam, Airflow, or Flink for streaming ingestion, transformation, and feature engineering.
• Applied NLP and/or computer vision experience extracting structured attributes (category, color, material, fit) from unstructured product descriptions and imagery.
• Strong API and infrastructure foundations: REST and GraphQL design with secure auth (OAuth/JWT), Git-based workflows, containerization with Docker and Kubernetes, and production observability with Grafana, Kibana, and APM tooling.
• Curiosity and pragmatism around emerging AI, particularly LLMs and modern retrieval/ranking techniques, with a track record of bringing new approaches into real production use cases.
• Strong written and verbal communication, able to explain technical tradeoffs to both technical and non-technical stakeholders, with a data-driven approach to problem solving.

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.

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 View email address on click.appcast.io.

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:

Pay Range

Salary: New York: $170,000 - $195,000 Remote US: $160,000 - $180,000

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