Job VC
Senior AI / Machine Learning Engineer
Technologies
Description
About Tie
Tie is building the next generation of identity resolution and marketing intelligence. Our platform connects hundreds of millions of consumers across devices, browsers, and channels—without relying on cookies—to power higher deliverability, smarter targeting, and measurable revenue lift for modern marketing teams.
At Tie, AI is not a feature—it is a core execution advantage. We operate large-scale identity graphs, real-time scoring systems, and production ML pipelines that directly impact revenue, deliverability, and customer growth.
The Role
We are looking for a
Senior AI / Machine Learning Engineer
to design, build, and deploy production ML systems that sit at the heart of our identity graph and scoring platform. You will work at the intersection of
machine learning, graph data, and real-time systems
, owning models end to end—from feature engineering and training through deployment, monitoring, and iteration.
This role is highly hands-on and impact-driven. You will help define Tie’s ML architecture, ship models that operate at sub-second latency, and partner closely with platform engineering to ensure our AI systems scale reliably.
What You’ll Do
Design and deploy
production-grade ML models
for identity resolution, propensity scoring, deliverability, and personalization
Build and maintain
feature pipelines
across batch and real-time systems (BigQuery, streaming events, graph-derived features)
Develop and optimize
classification models
(e.g., XGBoost, logistic regression) with strong handling of class imbalance and noisy labels
Integrate ML models directly with
graph databases
to support real-time inference and identity scoring
Own model lifecycle concerns: evaluation, monitoring, drift detection, retraining, and performance reporting
Partner with engineering to expose models via
low-latency APIs
and scalable services
Contribute to GPU-accelerated and large-scale data processing efforts as we push graph computation from hours to minutes
Help shape ML best practices, tooling, and standards across the team
What You’ll Bring
Required Qualifications
5+ years of experience building and deploying machine learning systems in production
Strong proficiency in
Python
for ML, data processing, and model serving
Hands-on experience with
feature engineering
, model training, and evaluation for real-world datasets
Ability to travel outside of Ukraine is a must
Experience deploying ML models via APIs or services (e.g., FastAPI, containers, Kubernetes)
Solid understanding of
data modeling, SQL
, and analytical workflows
Experience working in a cloud environment (GCP, AWS, or equivalent)
Experience with
graph data, graph databases, or graph-based ML
Familiarity with
Neo4j
, Cypher, or graph algorithms (community detection, entity resolution)
Preferred / Bonus Experience
Experience with
XGBoost
, tree-based models, or similar classical ML approaches
Exposure to
real-time or streaming systems
(Kafka, Pub/Sub, event-driven architectures)
Experience with
MLOps
tooling and practices (CI/CD for ML, monitoring, retraining pipelines)
GPU or large-scale data processing experience (e.g., RAPIDS, CUDA, Spark, or similar)
Domain experience in identity resolution, marketing technology, or email deliverability
Our Technology Stack
ML & Data:
Python, Pandas, Scikit-learn, XGBoost
Graphs:
Neo4j (Enterprise, GDS)
Cloud:
Google Cloud Platform (BigQuery, Vertex AI, Cloud Run, Pub/Sub)
Infrastructure:
Docker, Kubernetes, GitHub Actions
APIs:
FastAPI, REST-based inference services
What We Offer
Competitive compensation, including salary, equity, and performance incentives
Opportunity to work on
core AI systems
that directly impact revenue and product differentiation
High ownership and autonomy in a senior, hands-on role
Remote-first culture with a strong engineering and data focus
Exposure to cutting-edge problems in identity resolution, graph ML, and real-time AI systems
Clear growth path toward
Staff / Principal
IC roles
What else:
4 weeks of paid vacation per year (flexible scheduling)
Unlimited sick leave — we trust your judgment and care about your health
US Bank Holidays off (American calendar)
Remote-first culture and flexible working hours
Flat structure, no micromanagement, and full ownership
Opportunity to make a real impact during a critical growth phase
Interview Process
Recruitment Screening Call
Test task in Hackerrank for 1h
Technical deep dive Interview (1,5h) in English
Final interview with CTO in English
Why Join Us?
High-impact
delivery leadership role
during a critical period
Real ownership and autonomy
Opportunity to shape delivery across the entire engineering organization
Exposure to SaaS, data, integrations, automation, and platform work
Collaboration with global teams and vendors
A strong product with real scale and momentum
Why This Role Matters
At Tie, your work will not live in notebooks or experiments—it will power production systems used by real customers at scale. You will help define how AI is embedded into the company’s core platform and play a key role in making machine learning a durable competitive advantage.
Tie is building the next generation of identity resolution and marketing intelligence. Our platform connects hundreds of millions of consumers across devices, browsers, and channels—without relying on cookies—to power higher deliverability, smarter targeting, and measurable revenue lift for modern marketing teams.
At Tie, AI is not a feature—it is a core execution advantage. We operate large-scale identity graphs, real-time scoring systems, and production ML pipelines that directly impact revenue, deliverability, and customer growth.
The Role
We are looking for a
Senior AI / Machine Learning Engineer
to design, build, and deploy production ML systems that sit at the heart of our identity graph and scoring platform. You will work at the intersection of
machine learning, graph data, and real-time systems
, owning models end to end—from feature engineering and training through deployment, monitoring, and iteration.
This role is highly hands-on and impact-driven. You will help define Tie’s ML architecture, ship models that operate at sub-second latency, and partner closely with platform engineering to ensure our AI systems scale reliably.
What You’ll Do
Design and deploy
production-grade ML models
for identity resolution, propensity scoring, deliverability, and personalization
Build and maintain
feature pipelines
across batch and real-time systems (BigQuery, streaming events, graph-derived features)
Develop and optimize
classification models
(e.g., XGBoost, logistic regression) with strong handling of class imbalance and noisy labels
Integrate ML models directly with
graph databases
to support real-time inference and identity scoring
Own model lifecycle concerns: evaluation, monitoring, drift detection, retraining, and performance reporting
Partner with engineering to expose models via
low-latency APIs
and scalable services
Contribute to GPU-accelerated and large-scale data processing efforts as we push graph computation from hours to minutes
Help shape ML best practices, tooling, and standards across the team
What You’ll Bring
Required Qualifications
5+ years of experience building and deploying machine learning systems in production
Strong proficiency in
Python
for ML, data processing, and model serving
Hands-on experience with
feature engineering
, model training, and evaluation for real-world datasets
Ability to travel outside of Ukraine is a must
Experience deploying ML models via APIs or services (e.g., FastAPI, containers, Kubernetes)
Solid understanding of
data modeling, SQL
, and analytical workflows
Experience working in a cloud environment (GCP, AWS, or equivalent)
Experience with
graph data, graph databases, or graph-based ML
Familiarity with
Neo4j
, Cypher, or graph algorithms (community detection, entity resolution)
Preferred / Bonus Experience
Experience with
XGBoost
, tree-based models, or similar classical ML approaches
Exposure to
real-time or streaming systems
(Kafka, Pub/Sub, event-driven architectures)
Experience with
MLOps
tooling and practices (CI/CD for ML, monitoring, retraining pipelines)
GPU or large-scale data processing experience (e.g., RAPIDS, CUDA, Spark, or similar)
Domain experience in identity resolution, marketing technology, or email deliverability
Our Technology Stack
ML & Data:
Python, Pandas, Scikit-learn, XGBoost
Graphs:
Neo4j (Enterprise, GDS)
Cloud:
Google Cloud Platform (BigQuery, Vertex AI, Cloud Run, Pub/Sub)
Infrastructure:
Docker, Kubernetes, GitHub Actions
APIs:
FastAPI, REST-based inference services
What We Offer
Competitive compensation, including salary, equity, and performance incentives
Opportunity to work on
core AI systems
that directly impact revenue and product differentiation
High ownership and autonomy in a senior, hands-on role
Remote-first culture with a strong engineering and data focus
Exposure to cutting-edge problems in identity resolution, graph ML, and real-time AI systems
Clear growth path toward
Staff / Principal
IC roles
What else:
4 weeks of paid vacation per year (flexible scheduling)
Unlimited sick leave — we trust your judgment and care about your health
US Bank Holidays off (American calendar)
Remote-first culture and flexible working hours
Flat structure, no micromanagement, and full ownership
Opportunity to make a real impact during a critical growth phase
Interview Process
Recruitment Screening Call
Test task in Hackerrank for 1h
Technical deep dive Interview (1,5h) in English
Final interview with CTO in English
Why Join Us?
High-impact
delivery leadership role
during a critical period
Real ownership and autonomy
Opportunity to shape delivery across the entire engineering organization
Exposure to SaaS, data, integrations, automation, and platform work
Collaboration with global teams and vendors
A strong product with real scale and momentum
Why This Role Matters
At Tie, your work will not live in notebooks or experiments—it will power production systems used by real customers at scale. You will help define how AI is embedded into the company’s core platform and play a key role in making machine learning a durable competitive advantage.