Senior ML Engineer (ML/AI)

7Seventy; United States; Remote; USD 143,000 - 197,000; Full time

Benefits offered by this job

Healthcare coverage

401(k) matching

Monthly technology allowance

Paid vacation and holidays

Flexible Spending Account (FSA)

Job summary

7Seventy seeks a Senior ML Engineer to design, deploy, and optimize scalable ML/AI systems powering production applications. You will build DL/ML models, including LLMs, and work on RAG, NLP, and predictive analytics within a production-grade software team.

You will lead model training, evaluation, and deployment, collaborating with data scientists, engineers, and product teams to deliver reliable AI-powered features.

Qualifications

• 6+ years of professional experience deploying ML/AI solutions in production.
• Strong Python production code development.
• Experience building retrieval-augmented generation (RAG) solutions for AI-powered apps.
• Experience configuring and maintaining vector databases.
• Willingness to learn new technologies quickly.
• Strong communication and cross-functional collaboration.

Responsibilities

• Design, train, fine-tune, and evaluate DL/ML models including LLMs for production features.
• Develop, scale, and maintain ML pipelines for continuous training and evaluation.
• Build production-ready ML systems integrating analytics, NLP, and Generative AI.
• Collaborate with backend/frontend teams to integrate models into microservices with low latency.
• Architect scalable data pipelines for large datasets and vectorization.

Skills

Python

RAG

Vector databases

Communication

Fast-paced delivery

Tools

Docker

Kubernetes

Celery

Protobuf

AWS

Job description

Experience: 6+ years deploying ML/AI solutions in production environments

Core Areas: Machine Learning, Deep Learning, Generative AI (LLMs), Natural Language Processing (NLP), Python, MLOps, Retrieval-Augmented Generation (RAG), ML Pipelines

Compensation: $143,000-$197,000 annually

About the Role

The Senior ML Engineer (ML/AI) will design, develop, and deploy scalable machine learning and artificial intelligence systems that power production applications. This position focuses on building and optimizing deep learning, classical machine learning, and Generative AI models, including large language models (LLMs), to support predictive analytics, natural language processing, and intelligent product capabilities. Key responsibilities include model training, fine-tuning, evaluation, production deployment, and the development of reliable ML infrastructure.

The role combines applied machine learning with production-grade software engineering, using Python, MLOps frameworks, retrieval-augmented generation (RAG), vector databases, and scalable data pipelines. The engineer will collaborate with data scientists, data analysts, backend and frontend engineers, product managers, physicians, and therapists to translate AI research into dependable applications. Responsibilities also include technical leadership, architecture decisions, code reviews, mentoring engineers, and contributing to the AI product roadmap.

What You'll Do

• Design, train, fine-tune, and evaluate deep learning, classical machine learning, and foundation models, including large language models (LLMs) and diffusion models, to support production AI features.
• Develop, scale, and maintain ML pipelines for continuous model training, automated evaluation, and real-time and batch inference using modern MLOps frameworks.
• Build production-ready machine learning systems that integrate predictive analytics, natural language processing (NLP), and Generative AI capabilities into software applications.
• Partner with backend and frontend engineering teams to integrate AI models into microservices while maintaining low latency, high availability, and efficient resource utilization.
• Architect scalable data pipelines for preprocessing, vectorization, and ingestion of large structured and unstructured datasets.
• Establish rigorous model evaluation frameworks covering alignment, bias mitigation, guardrails, and production reliability.
• Optimize model performance, inference latency, and operational costs using techniques such as quantization and distillation.
• Bridge data science research and production software engineering by developing scalable architectures that support reliable model deployment.
• Provide technical leadership, conduct detailed code reviews, and mentor junior and mid-level engineers in machine learning engineering and software development best practices.
• Collaborate with cross-functional technical, product, and clinical stakeholders to translate complex requirements into practical AI solutions and influence the AI product roadmap.

Qualifications

Required Experience

• 6+ years of professional experience deploying machine learning or artificial intelligence solutions in production environments.

Required Skills

• Ability to develop high-quality, maintainable production code in Python.
• Practical experience building retrieval-augmented generation (RAG) solutions for AI-powered applications.
• Experience setting up, configuring, and maintaining vector databases.
• Strong interest in developing machine learning and AI-based products and applying emerging technologies to practical problems.
• Ability and willingness to learn new technologies quickly and apply them effectively.
• Sound judgment in balancing software quality, technical requirements, and delivery deadlines in fast-paced environments.
• Excellent communication skills, including the ability to establish consensus and alignment across technical and cross-functional teams.
• Strong organizational skills and the ability to simplify complex problems, prioritize competing requirements, and identify actionable next steps.

Preferred Qualifications

• Experience defining and working with Protocol Buffers (Protobuf) messages.
• Experience using Docker and deploying applications with Kubernetes.
• Familiarity with relational databases and low-latency database systems.
• Experience using Celery for application task processing.
• Additional hands-on experience implementing RAG architectures and managing vector database infrastructure.
• Experience writing production software in Java or Kotlin.
• Experience building and deploying applications on cloud infrastructure, particularly Amazon Web Services (AWS).
• Experience developing technical solutions involving highly sensitive healthcare data.
• Comprehensive healthcare coverage, including medical, dental, and vision insurance.
• Flexible Spending Account (FSA) and Health Savings Account (HSA) options.
• Life insurance and disability insurance.
• Employee access to coaching and therapy services.
• Potential equity compensation through discretionary restricted stock units.
• Paid vacation, sick leave, and company holidays.
• Paid parental leave.
• 401(k) retirement plan with up to 3% employer matching.
• Monthly technology allowance.
• Employee well-being perks, activities, and recognition programs.