Senior Software Engineer 1, ML

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 target="_blank">hr@people.inc.

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  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 your work location. We also offer voluntary benefits such as pet insurance, accident, critical and hospital indemnity health insurance coverage, life and disability insurance.

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