Company
Micro1
Annual salary
$200k – $350k/yr
Location
hybrid
Listed
39d ago
- Min. degree:
- Master's
- Level:
- Mid
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Join our team as a AI/ML Engineer and play a pivotal role in driving mission-critical government initiatives while also contributing to micro1’s proprietary AI infrastructure, you’ll harness advanced large language models (LLMs), retrieval-augmented generation (RAG), and multi-agent orchestration to deliver secure, scalable, and impactful machine learning systems. You’ll work closely with cross-functional experts, leveraging frontier AI platforms and the latest in agentic AI tooling.
What you'll do
- Design, implement, and optimize AI/ML models particularly leveraging LLMs, RAG, and prompt engineering for production grade applications.
- Develop and orchestrate multi-agent systems using frameworks such as LangGraph and LangChain.
- Integrate and deploy solutions on secure cloud environments, including AWS GovCloud, Google GovCloud, Azure IL5+, Vertex AI, and AWS Bedrock.
- Build robust data pipelines, manage ETL processes, and develop metadata catalogs and ontologies to ensure high-quality data for AI training and inference.
- Create and maintain REST APIs and SDK integrations to facilitate seamless data and model interactions.
- Collaborate with product, security, and engineering teams to ensure best-in-class delivery, following secure coding and DevOps best practices (CI/CD).
- Document technical decisions and communicate complex concepts clearly to both technical and non-technical stakeholders.
What you need
- Proficiency in Python for AI/ML development, including experience with REST APIs and SDK integration.
- Hands-on experience with LLMs, RAG systems, and prompt engineering in production environments.
- Familiarity with multi-agent orchestration, tool use, and frameworks like LangGraph and LangChain.
- Deep understanding of cloud AI services (AWS GovCloud, Google GovCloud, Azure IL5+, Vertex AI, AWS Bedrock).
- Background in building and maintaining data pipelines, ontologies, metadata catalogs, and ETL processes.
- Strong grasp of secure coding and modern DevOps practices (CI/CD pipelines).
- Exceptional written and verbal communication skills for effective collaboration and documentation.
Nice to have
- Experience with AI/agentic platforms, such as Anthropic for Gov, OpenAI Enterprise, Gemini Enterprise, or Grok Enterprise.
- Knowledge of metadata catalog platforms (MCP) and advanced API development techniques.
- Prior exposure to government or highly regulated cloud environments and compliance standards.
About Micro1 and legal notices
About Us
micro1 is the end-to-end human data infrastructure behind AGI. Our AI recruiter model is used by frontier AI labs and Fortune 10s to source, vet, and deploy PhDs and professors from the world’s top universities at scale. These experts are placed directly into the training loops of the most advanced AI systems, powering the breakthroughs that move models forward. Our data platform converts their expertise into high-signal training datasets, and our talent management tooling measures, routes, and improves performance at scale.
Skills & Categories
Frequently Asked Questions
How do I apply for the AI/ML Engineer role at Micro1? +
Use the Apply button on this page. It opens Micro1's own application form, so you apply directly with them.
What does this Micro1 role pay? +
Micro1 lists $200k – $350k/yr.
Is this role remote? +
The listing gives the location as hybrid. Check Micro1's posting for whether remote work is allowed.
Do I need a degree? +
The listing asks for a Master's or equivalent. Micro1 makes the final call on what counts.
Interview Prep
Sample questions for a AI Engineer role, written in-house to help you prepare.
How do you decide which evaluation metric to optimize for a classification model when the classes are imbalanced?
Accuracy is misleading on imbalanced data because a model can score well by always predicting the majority class. I look at precision, recall, and F1 for the minority class specifically, and pick the metric that matches the real cost of false positives versus false negatives in the deployment context, since those costs are rarely symmetric in practice.
What is your process for debugging a deep learning model that trains fine but performs poorly at inference time?
I start by checking for a train/inference mismatch: different preprocessing, batch normalization behaving differently in eval mode, or data leakage during training that inflated validation scores. Comparing the exact input pipeline used at training time against the one used at inference usually surfaces the discrepancy before I need to touch the model architecture itself.
How do you approach feature selection when working with high-dimensional tabular data?
I start with correlation and mutual information analysis to drop obviously redundant features, then use a tree-based model's feature importance as a first pass filter. From there, recursive feature elimination with cross-validation tells me where the marginal value of additional features drops off, which keeps the final feature set both smaller and more robust to overfitting.
Explain how you would detect and address data drift in a production machine learning pipeline.
I monitor the statistical distribution of input features and model outputs over time, using something like population stability index or KL divergence against a reference window. When drift crosses a threshold, I retrain on recent data rather than the full historical set, and I keep an alert in place so drift is caught before it shows up as a quality regression.
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