Head of ML & Data Engineering

Vacancy Name
Head of ML & Data Engineering

Vacancy No
VN364

Status
Active

Location
Remote US

Location Country
United States

Location Region

Location City

Description
Who are we?


Auction Technology Group (ATG) is transforming the multi-billion-dollar global auction industry. Our platforms connect thousands of auction houses with buyers in over 170 countries, powering more than $15 billion in annual sales. Through innovative online auction technologies, we help auctioneers expand their reach, boost efficiency, and maximize value-while giving bidders unrivaled access to rare and specialized items. As a publicly traded company, ATG has scaled from $18 million to $170 million in revenue, with sustained growth beyond the pandemic. We're modernizing one of the last industries to fully go digital-building a global, category-defining business in the process.


What are we hiring for?


We are making a significant investment in building a world-class ML team. The Engineering Manager will lead a cross-functional team of MLOps Engineers, Data Engineers, and ML Engineers responsible for building and scaling the infrastructure that powers our AI/ML capabilities, data pipelines, and real-time analytics across all of our marketplaces. This role requires both deep technical expertise and strong people leadership skills to build and scale a high-performing team.

Key Responsibilities
What will you do?


Team Leadership & Development


Build, mentor, and scale a team of MLOps Engineers, Data Engineers, and ML Engineers


Create career development plans, conduct performance reviews, and provide regular coaching and feedback


Foster a culture of technical excellence, collaboration, experimentation, and continuous improvement


Remove blockers and create an environment where engineers can do their best work


Technical Strategy & Architecture


Define the technical vision and roadmap for ML infrastructure, data pipelines, and enablement capabilities


Drive architectural decisions for embedding systems, recommendation engines, search integration, and feature stores


Establish standards and best practices for MLOps including model versioning, deployment and monitoring


Balance technical debt with feature delivery while maintaining system reliability and scalability


Stay current with ML infrastructure trends and evaluate new technologies for potential adoption


Delivery & Execution


Own the planning, prioritization, and delivery of ML infrastructure and data pipeline initiatives


Collaborate with Product Managers to translate business requirements into technical solutions


Work closely with cross-functional teams (Software Engineering, Analytics, Data Science) to ensure seamless integration


Establish metrics and KPIs to measure team performance, system health, and business impact


Drive incident response and post-mortems for ML/data systems, implementing preventive measures


Manage stakeholder expectations and communicate progress, risks, and trade-offs effectively


Operational Excellence


Ensure high availability, reliability, and performance of ML systems handling millions of daily requests


Establish monitoring, alerting, and observability practices for ML models and data pipelines


Implement CI/CD practices for ML workflows including testing, versioning, and deployment strategies


Drive cost optimization efforts across cloud infrastructure and data storage

Key Requirements
What do we need from you?


Leadership & Management Experience:


5+ years managing engineering teams, preferably in ML/MLOps or data engineering domains


Proven track record of building and scaling high-performing teams


Experience hiring, mentoring, and developing engineers at various career stages


Strong emotional intelligence with ability to give and receive constructive feedback


Technical Expertise:


7+ years of hands-on software engineering experience with at least 3+ years in ML infrastructure or data engineering


Deep understanding of ML lifecycle: data preparation, feature engineering, model training, deployment, monitoring


Strong experience with MLOps tools and platforms (MLflow, Kubeflow, feature stores, model registries)


Expertise in building scalable data pipelines using tools like Airflow, Dagster, or similar orchestration frameworks


Hands-on experience with embedding systems, vector databases, and search technologies (Elasticsearch, OpenSearch)


Proficiency in Python and SQL with experience building production-grade systems


Strong knowledge of cloud platforms (AWS preferred) including services like S3, EMR, SageMaker


Past work designing data models and optimizing query performance within Snowflake


Experience with containerization (Docker), orchestration (Kubernetes), and infrastructure-as-code (Terraform, CloudFormation)


Domain Knowledge:


Understanding of recommendation systems, ranking algorithms, and personalization techniques


Experience with A/B testing frameworks and experimentation platforms for ML models


Familiarity with data streaming technologies (Kafka) and real-time processing


Knowledge of data governance, privacy regulations, and security best practices


Experience working in eCommerce, marketplaces, or consumer-facing products is a plus


Soft Skills & Attributes:


Excellent communication skills with ability to explain complex technical concepts to diverse audiences


Strategic thinking balanced with tactical execution and attention to detail


Collaborative mindset with experience working across Product, Engineering, Data Science, and Analytics teams


Data-driven decision-making approach with strong analytical and problem-solving abilities


Comfortable operating in ambiguous environments and adapting to changing priorities


Passion for enabling others and driving impact through people and technology


Strong organizational skills with ability to manage multiple competing priorities


Nice-to-Have:


Experience in auction, marketplace, or eCommerce platforms


Experience with computer vision or NLP systems


Familiarity with dbt (data build tool) for analytics engineering workflows

Auction Technology Group is committed to fair and equitable compensation practices.

The pay range for this role is $200,000 to $225,000. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to skill set, depth of experience, certifications, and specific work location. This may be different in other locations due to differences in the cost of labor.

The total compensation package for this position may also include annual performance bonus, stock, benefits and/or other applicable incentive compensation plans.

Employment Type
Permanent

Duration

Business Name
Proxibid

Function Name
Technology