Senior Machine Learning Engineer

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. Design and build RAG pipelines, including chunk-store schemas, Redshift-sourced population, and signal-based retrieval .

Posted 11/3/2026full-timeRemote • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and maintaining machine learning pipelines, including retrieval systems and embedding frameworks, while leveraging tools like SageMaker and Bedrock. Proficient in integrating ML models into data pipelines and ensuring production readiness through robust evaluation and orchestration.

Highest-signal resume keywords

Machine Learning EngineeringRetrieval Systems ExpertisePython ProgrammingAWS ML InfrastructureWorkflow Orchestration

ATS Keywords

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Hard Skills

ML EngineeringModel ProductionizationRetrieval SystemsEmbeddingsVector SimilarityBM25ChunkingRerankingData EngineeringFeature Extraction

Tools & Technologies

SageMakerBedrockOpenAIPyTorchScikit-learnHugging FaceXGBoostAirflowPrefectKubeflow

Industry Keywords

RAG PipelinesHybrid RetrievalANN SearchVector DatabasesCDC-based FreshnessA/B TestingSearch APIsAnalytics APIs

Tech Stack

Tools & technologies

AirflowAmazon RedshiftAssemblyAWSPythonPyTorchScikit-LearnSparkSQL

About the role

Key responsibilities & impact

Design and build RAG pipelines, including chunk-store schemas, Redshift-sourced population, and signal-based retrieval
Own semantic search infrastructure, including embedding pipelines, chunking, hybrid BM25/vector retrieval, and reranking
Build recommendation models end-to-end, including feature extraction, training, evaluation, and SageMaker inference
Design tool definitions, prompt assembly, and context selection for Bedrock-backed AI features
Compute and serve ML features such as historical aggregates, behavioral profiles, and engagement history
Build and maintain batch and incremental Bedrock embedding pipelines with CDC-based freshness
Build evaluation frameworks for retrieval metrics, model quality tracking, and A/B testing
Collaborate with data engineers to integrate ML models into data pipelines and expose results through search and analytics APIs

Requirements

What you’ll need

4+ years in ML engineering
Experience taking models or ML features from experimentation into production
Expertise in retrieval systems, embeddings, vector similarity, ANN search, BM25, dense retrieval, chunking, reranking, and vector databases such as PGvector, FAISS, Qdrant, Weaviate, and Pinecone
Hands-on experience with RAG, tool use/function calling, prompt engineering, and context assembly
Experience with Bedrock, OpenAI, Anthropic SDK, or LangChain
Strong Python and production ML tooling experience with PyTorch, scikit-learn, Hugging Face, and XGBoost
Data engineering fundamentals, including SQL or Spark, point-in-time correctness, and feature leakage awareness
AWS ML infrastructure experience with SageMaker, Bedrock, S3, and Lambda
Workflow orchestration experience with Airflow, Prefect, or Kubeflow
Ability to build reliable scheduled ML pipelines that handle failures without manual intervention

Benefits

Comp & perks

Flexible remote work
Competitive base salaries
Flexible PTO with no set number of days
Paid maternity, parental bonding, and medical leave
Medical, Dental, Vision, and Prescription coverage; employee-only plans cover 100% of premiums and family coverage costs are covered at 50%
Free confidential counseling through BetterHelp
401(k) with 100% employer match on contributions up to 6%
Flexible Spending Accounts (FSA) and Health Savings Accounts (HSA)
Life and AD&D Insurance
Up to $60/month toward fitness, mental health, or other wellness expenses
$300 home office setup bonus after one year of employment
Continuing education support
Team and company-wide offsites, with attendance fully supported