Axiomatic_AI

Axiomatic AI is pioneering a new class of AI called Axiomatic Intelligence (AxI) that integrates formal verification with deep learning to produce verifiable, interpretable, and logically rigorous AI…

AI-driven DesignEngineering InnovationFormal VerificationPhotonic HardwarePhysics-based ModelsScientific InnovationSemiconductor HardwareVerifiable AIaxiomatic-ai.com

Axiomatic_AI

AI-driven DesignEngineering InnovationFormal VerificationPhotonic HardwarePhysics-based ModelsScientific InnovationSemiconductor HardwareVerifiable AIaxiomatic-ai.com

HQBoston, US

Team Size38

Open Jobs9

Total Funding$18M

Latest Fundraise4 months ago

Join the Team

AI Platform Engineer

On-SiteBoston, MA, US

On-Site • Boston, MA, US

Position Overview As an AI Platform Engineer, you are the bridge between AI research and production software. You will:

Build and maintain AI infrastructure: model serving, vector databases, embedding pipelines
Enable AI developers to deploy their work reproducibly and safely
Design APIs for AI inference, prompt management, and evaluation
Implement MLOps pipelines: versioning, monitoring, logging, experimentation tracking
Optimize performance: latency, cost, throughput, reliability
Collaborate with backend engineers to integrate AI capabilities into the product

Key Responsibilities

Required Skills & Experience Must-Have

7+ years of software engineering experience (Python preferred)
Experience with LLMs and AI/ML in production: OpenAI API, HuggingFace, LangChain, or similar
Understanding of vector databases (Pinecone, Chroma, Weaviate, FAISS)
Cloud infrastructure experience: GCP (Vertex AI preferred) or AWS (SageMaker)
API development: FastAPI, REST, async programming
CI/CD and DevOps: Docker, Terraform, GitHub Actions
Monitoring and observability
Problem-solving mindset: comfortable debugging complex distributed systems
Operating experience with AI deployment in enterprise environment

Nice-to-Have

Experience fine-tuning or training models
Familiarity with LangChain, Pydantic AI or similar frameworks
Knowledge of prompt engineering and evaluation techniques
Experience with real-time inference and streaming responses
Background in data engineering or ML engineering
Understanding of RAG architectures
Contributions to open-source AI/ML projects

Tech Stack Current Stack:

Languages: Python (primary), Bash
AI/ML: OpenAI API, Anthropic, HuggingFace, LangChain, Pydantic AI

What we offer: Competitive compensation

Stock Options Plan: Empowering you to share in our success and growth.
Cutting-Edge Tools: Access to state-of-the-art tools and collaborative opportunities with leading experts in artificial intelligence, physics, hardware and electronic design automation.
Work-Life Balance: Flexible work arrangements in one of our offices with potential options for remote work.
Professional Growth: Opportunities to attend industry conferences, present research findings, and engage with the global AI research community.

Startup jobs. A lot of them.

Your next opportunity is in here somewhere. Sign up to explore 70,000+ startups and their open roles. No spam. No gamification. Just jobs.

70,000+

Startups

82,000+

Open Roles

4,600+

New This Week

Data Scientist

ContractTel Aviv

Contract • Tel Aviv

Product Designer

Part-timeNiš, RS

Part-time • Niš, RS

Machine Learning Engineer

ContractAmsterdam, NL

Contract • Amsterdam, NL

DevOps Engineer

Part-timeHaifa

Part-time • Haifa

Frontend Developer

Part-timeSan Francisco, US

Part-time • San Francisco, US

Mobile Developer

Part-timeMunich, DE

Part-time • Munich, DE

AI Infrastructure

Deploy and serve LLMs (OpenAI, Anthropic, HuggingFace, fine-tuned models)

Optimize inference latency and costs

Implement caching, rate limiting, and retry strategies

MLOps & Pipelines

Version models, prompts, datasets, and evaluation results

Implement experiment tracking (Weights & Biases)

Build CI/CD pipelines for model deployment

Monitor model performance and drift

Set up logging and observability for AI services

Design and implement APIs (FastAPI)

Create endpoints for prompt testing, model selection, and evaluation

Integrate AI services with backend application

Ensure API reliability, security, and performance

Collaboration & Enablement

Work with AI Developers to productionize their experiments regarding improving user workflows

Define workflows: notebook/test repository → PR → staging → production

Document AI infrastructure and best practices

Review code and mentor AI developers on software practices

Vector DBs: Pinecone, Chroma, Weaviate, or FAISS

Backend: FastAPI, SQLAlchemy, Pydantic

Cloud: GCP (Vertex AI, Cloud Run), Terraform

CI/CD: GitHub Actions

Experiment Tracking: MLflow, Weights & Biases, or custom

Containers: Docker, Kubernetes (optional)

Impact-Driven Culture: Join a passionate team focused on solving some of the most challenging problems at the intersection of AI and hardware.