Lead AI/ML Engineer

arkestroinc; United States; Remote; USD 160,000 - 200,000; Full time

Benefits offered by this job

Startup equity

Medical, dental, vision coverage

Retirement matching

Unlimited PTO

Learning & development budget

Job summary

Arkestro is seeking a senior ML engineering leader to own substantial parts of the design, implementation, and operation of machine learning and agent-based AI features in a production software product. This broad, hands-on technical leadership role spans product integration, evaluation, data pipelines, and production tooling, influencing an evolving AI/ML architecture.

Success means delivering useful capabilities while treating reliability, safety, latency, and cost as core engineering

Qualifications

• Six or more years in data science, ML engineering, or related field with hands-on production delivery.
• Strong software engineering ability, especially Python, and experience shipping ML models or AI features.
• Experience creating or curating evaluation datasets or ground truth for ML/LLM systems.
• Familiarity with MLOps practices, cloud platforms (preferably AWS), and data warehousing.
• Understanding of ML concepts and practical production use, with version control and CI/CD.
• Ability to lead technical work independently and communicate clearly.

Responsibilities

• Build and productionize ML models and agentic AI features in the product.
• Shape AI/ML architecture and contribute to data transformation and pipelines.
• Define evaluations and ground truth with PMs; curate evaluation datasets.
• Develop and maintain production tooling with focus on latency, cost, and reliability.
• Establish safety safeguards and readiness standards for AI features.
• Advise engineers on AI/ML practices and communicate with technical and nontechnical stakeholders.

Skills

Python

Machine learning

Data science

MLOps

AWS

CI/CD

Data warehousing

Leadership

Education

Master's degree in a relevant field

PhD desirable

Tools

Docker

Kubernetes

dbt

Job description

Role overview

Own substantial parts of the design, implementation, and operation of machine learning and agent-based AI features in a production software product. This broad, hands-on technical leadership role spans product integration, evaluation, data pipelines, and production tooling, with influence over an evolving AI/ML architecture. Success means delivering useful capabilities while treating reliability, safety, latency, and cost as core engineering concerns.

Responsibilities

• Build and productionize machine learning models and agentic AI functionality, partnering with product engineering teams to integrate features into the product.
• Help shape AI/ML architecture and contribute to data transformation and pipeline work when required by the systems being built.
• Define project evaluations and ground truth with product managers and stakeholders; curate human-generated evaluation sets and support automated evaluation.
• Develop and maintain production tooling, with attention to latency, operating cost, error handling, and dependable releases.
• Establish appropriate safety and responsible-AI safeguards, identify risks, and help define readiness standards for AI features.
• Advise engineers on AI/ML practices, communicate with technical and nontechnical partners, and sometimes explain functionality to customers or customer-facing teams.

Requirements

• Six or more years in data science, machine learning engineering, or a related quantitative or engineering field, including hands-on production delivery.
• Strong software engineering ability, especially in Python, and direct experience shipping ML models or AI features.
• Experience creating or curating evaluation datasets or ground truth for machine learning or LLM systems.
• Familiarity with MLOps practices, cloud platforms (preferably AWS), and data warehousing.
• Understanding of machine learning concepts and their practical production use, along with version control and CI/CD.
• Ability to lead technical work independently, influence direction without formal authority, and communicate clearly.

Nice to have

• Experience embedding LLM or agent-based features in a production product, using dbt, or working with Docker and Kubernetes.
• Mentoring experience, knowledge of procurement or supply-chain data, or experience in enterprise software and startups.

Benefits and work setup

Remote-first work with regular in-person gatherings for team activities and customer visits. The package includes startup equity, medical, dental, and vision premiums covered up to 100% for employee-only coverage, discretionary retirement matching, unlimited paid time off, and a $1,000 annual learning and development budget. The stated salary range is $160,000–$200,000.