AI/ML Engineer (US)

Latitude; San Francisco (CA); Hybrid; USD 130,000 - 220,000; Full time

Job summary

Latitude is seeking an AI/ML Engineer to design, develop, and deploy scalable ML/AI solutions across the product and business stack. You will work across the full lifecycle—from data prep to deployment, monitoring, and optimization.

The role emphasizes strong software engineering skills with practical experience in ML, deep learning, generative AI, and production AI systems. Leadership responsibilities may be assigned for senior engineers.

Qualifications

• 2–8+ years of professional experience in AI/ML or related field.
• Strong Python production-quality software skills.
• Experience with ML frameworks PyTorch, TensorFlow, or scikit-learn.
• Experience deploying ML models in production.

Responsibilities

• Design, develop, and deploy production-grade ML/AI systems.
• Build and optimize ML models for real-world product applications.
• Develop generative AI and LLM-powered applications (RAG, embeddings, agents).
• Prepare, process, and analyze large datasets for training and inference.
• Build scalable data and ML pipelines for experimentation and production workloads.
• Integrate ML models into reliable APIs, applications, and backend services.
• Develop model evaluation, testing, monitoring, and observability processes.
• Collaborate with engineers, data scientists, product managers, and stakeholders.
• Participate in architecture discussions and code reviews.
• Research emerging AI technologies and apply them to business problems.
• Senior candidates: provide technical leadership and mentor engineers.

Skills

Python

ML engineering

PyTorch

TensorFlow

scikit-learn

REST APIs

Docker

Git

CI/CD

Distributed systems

Tools

Kubernetes

Hugging Face

LLMs

Vector databases

Job description

AI/ML Engineer

Experience: 2–8+ years

Location: San Francisco Bay Area, Seattle, New York, Boston, Austin

Salary: $130,000–$220,000+ base salary

Employment Type: Full-time

About The Role

We are seeking an AI/ML Engineer to build and deploy scalable machine learning and artificial intelligence solutions that power our products and business operations.

You will work across the full AI/ML lifecycle—from data preparation and model development to deployment, evaluation, monitoring, and optimization. Depending on your experience level, you may also take ownership of technical architecture, mentor engineers, and lead high-impact AI initiatives.

This role is ideal for an engineer who combines strong software engineering skills with practical experience in machine learning, deep learning, generative AI, and production AI systems.

Key Responsibilities

• Design, develop, and deploy production-grade machine learning and AI systems.
• Build and optimize ML models for real-world product and business applications.
• Develop generative AI and LLM-powered applications, including RAG, embeddings, agents, and model integrations.
• Prepare, process, and analyze large datasets for training and inference.
• Build scalable data and ML pipelines for experimentation and production workloads.
• Integrate AI/ML models into reliable APIs, applications, and backend services.
• Develop robust model evaluation, testing, monitoring, and observability processes.
• Optimize models and inference systems for accuracy, latency, scalability, and cost.
• Collaborate with software engineers, data scientists, product managers, and other stakeholders.
• Participate in architecture discussions, technical design, code reviews, and engineering best practices.
• Research emerging AI/ML technologies and identify opportunities to apply them to business problems.
• For senior-level candidates, provide technical leadership, mentor engineers, and drive end-to-end delivery of complex AI initiatives.

Required Qualifications

• 2–8+ years of professional experience in AI/ML, machine learning engineering, software engineering, or a related field.
• Strong programming skills in Python and experience writing production-quality software.
• Solid understanding of machine learning, deep learning, statistics, and model evaluation.
• Hands-on experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn.
• Experience deploying and operating ML models in production environments.
• Experience with cloud platforms such as AWS, GCP, or Azure.
• Strong understanding of APIs, databases, data pipelines, and distributed systems.
• Experience with Git, Docker, CI/CD, and modern software development practices.
• Strong problem-solving, communication, and collaboration skills.

Experience In The Following Areas Is Highly Desirable

• Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG)
• Prompt engineering and LLM evaluation
• Embeddings and vector databases
• AI agents and tool/function calling
• Model fine-tuning and adaptation
• Hugging Face and/or commercial LLM APIs
• LLM application monitoring and observability
• AI safety, guardrails, and responsible AI practices

Preferred Qualifications

• Experience building large-scale production AI/ML systems.
• Experience with Kubernetes and cloud-native infrastructure.
• Experience with model serving and inference optimization.
• Familiarity with ML/LLMOps, experiment tracking, and model lifecycle management.
• Experience with distributed computing and GPU-based workloads.
• Experience with vector search, semantic search, or recommendation systems.
• Strong understanding of software architecture and scalable system design.
• For senior candidates: experience leading projects, mentoring engineers, or defining technical strategy.

Typical Technology Stack

Languages: Python, SQL

ML/AI: PyTorch, TensorFlow, scikit-learn, Hugging Face

Generative AI: LLMs, RAG, embeddings, agents, vector search

Cloud: AWS, GCP, Azure

Infrastructure: Docker, Kubernetes, CI/CD

Data: SQL, data pipelines, distributed data systems

Engineering: REST APIs, Git, testing, monitoring, observability

What We’re Looking For

We Value Engineers Who

• Think beyond experimentation and can ship AI systems to production.
• Balance model quality with performance, reliability, and cost.
• Are comfortable working with ambiguous and complex technical problems.
• Learn quickly and stay current with rapidly evolving AI technologies.
• Communicate clearly with both technical and non-technical stakeholders.
• Take ownership and can work effectively in a fast-paced environment.

Compensation

Base Salary: $130,000–$220,000+

Compensation will be determined based on experience, technical expertise, seniority, location, and scope of responsibility. Additional compensation may include equity, bonuses, and comprehensive benefits, depending on the role and employment package.

Location

This Position Is Open To Candidates Based In

• San Francisco Bay Area
• Seattle
• New York
• Boston
• Austin

Remote or hybrid arrangements may be considered depending on team and business requirements.

Equal Opportunity

We are committed to building an

Skills: llms,nlp,machine learning,pytorch,tensorflow,python,deep learning