Machine Learning Engineer at Latent AI

San Francisco, California, United States

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Not SpecifiedCompensation

Junior (1 to 2 years)Experience Level

Full TimeJob Type

UnknownVisa

Healthcare, Artificial Intelligence, SoftwareIndustries

Requirements

Strong foundation in ML research or systems (advanced degree or high-impact work in industry)
Deep experience with NLP and LLMs (fine-tuning, LoRA, RAG, quantization)
Experience shipping ML models in production environments (latency, safety, or interpretability critical)
Ability to think creatively about tradeoffs between performance, explainability, and reliability

Responsibilities

Train and fine-tune large open-source language models for clinical reasoning, medical question answering, and evidence-grounded generation
Design and scale multimodal embeddings to encode clinical documents, structured EHRs, and payer policies
Own the lifecycle of ML systems—from research prototypes to fault-tolerant, privacy-compliant services
Build robust retrieval pipelines for real-time semantic search and RAG architectures in the clinical domain
Collaborate with clinicians, engineers, and product leaders to ensure outputs are interpretable, auditable, and aligned with real-world constraints
Contribute to a culture of ML excellence through code reviews, experimentation frameworks, and internal knowledge sharing

Skills

Machine Learning

Large Language Models

Medical Language Understanding

Model Training

Model Fine-tuning

Multimodal Embeddings

EHR Data

Data Engineering

Production Systems

Retrieval Pipelines

Latent AI

Optimizes AI for edge computing applications

About Latent AI

Latent AI focuses on enhancing artificial intelligence for edge computing, which involves processing data close to where it is generated, like on smartphones and IoT devices. Their main product is an edge MLOps workflow that automates and simplifies the deployment of AI models on these devices, ensuring they are efficient, secure, and require minimal maintenance. This company stands out from competitors by specifically targeting the edge computing market and providing a software development kit (SDK) called Latent LEIP, which aids developers in creating and deploying AI models. The goal of Latent AI is to make AI more accessible and efficient for applications that need real-time data processing, such as in autonomous vehicles and smart cities.

Menlo Park, CaliforniaHeadquarters

2018Year Founded

$21.9MTotal Funding

SERIES_ACompany Stage

Automotive & Transportation, Enterprise Software, AI & Machine LearningIndustries

51-200Employees

Risks

Competition from tech giants like Google and Amazon threatens Latent AI's market share.

Rapid AI advancements may outpace Latent AI's current capabilities, requiring continuous innovation.

Dependency on key partnerships, like Booz Allen, poses risks if relationships change.

Differentiation

Latent AI specializes in optimizing AI for edge computing applications.

Their edge MLOps workflow ensures efficient, secure AI model deployment on edge devices.

Latent AI's SDK, Latent LEIP, streamlines AI model creation and deployment for developers.

Upsides

Growing interest in federated learning enhances privacy and efficiency for edge AI.

The rise of TinyML aligns with Latent AI's focus on resource-constrained edge devices.

5G technology adoption boosts data transmission speed for Latent AI's solutions.

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