Job VC
Senior Machine Learning Engineer
Technologies
Description
We’re looking for a
Senior Machine Learning Engineer
to design and scale production ML systems that power real-time personalization and decision-making at scale.
In this role, you’ll own the
full ML lifecycle
—from transforming raw behavioral data into meaningful features, to deploying low-latency prediction APIs, to building the observability needed to keep models reliable in production.
This is a great opportunity for someone with strong
applied ML and MLOps expertise
who enjoys solving complex engineering challenges and building scalable, high-impact systems.
Your responsibilities will include:
Build and productionize ML models for ranking, personalization, and customer engagement.
Develop pipelines that transform behavioral, demographic, and contextual signals into online and offline features.
Design and deploy low-latency APIs and decision services for real-time decision-making.
Implement experimentation frameworks, including A/B testing and exploration-exploitation strategies.
Operationalize the ML lifecycle: automated training, CI/CD for models, artifact and feature versioning, and online/offline parity.
Build observability into ML systems by monitoring data quality, model drift, and decision outcomes, and triggering retraining when needed.
Establish closed feedback loops that connect decisions to business outcomes (e.g. conversions, engagement, fatigue signals such as unsubscribes).
Collaborate closely with product and engineering teams to balance personalization, compliance, and business value in real-world systems.
What we expect from you:
5+ years
of experience in applied ML engineering (recommendation systems, personalization, ranking, or advertising systems).
Strong proficiency in
Python or Go
, SQL, and modern ML frameworks such as
TensorFlow, PyTorch
, or similar.
Strong understanding of
MLOps best practices
, including CI/CD for ML, containerization (
Docker
), orchestration (
Kubernetes, Airflow, Kubeflow
), model registries, and monitoring frameworks.
Familiarity with cloud ML platforms such as
Vertex AI, SageMaker
, or similar, and data warehouses like
BigQuery, Snowflake, or Redshift
.
Experience deploying
real-time ML systems
, including low-latency serving, feature stores, and event-driven architectures.
Understanding of
multi-objective optimization
and trade-offs in personalization systems.
Comfort working cross-functionally in a dynamic startup environment with the
overlap
within USA time zone.
Strong spoken and written
English communication skills
.
Nice to have:
Experience in
martech, adtech, CRM
, or large-scale personalization platforms.
Exposure to
bandit algorithms, reinforcement learning, or causal inference
for adaptive decision-making.
Experience building systems serving
millions of users at scale
.
Hands-on experience with
Google Cloud Platform (GCP)
.
Familiarity with observability tools such as
Prometheus, Grafana, Evidently, WhyLabs,
or
Great Expectations
for monitoring data and model health.
What we offer:
Interesting projects and technical challenges that support both professional and personal growth.
A long-term project with stability and impact.
A flexible, results-oriented schedule with hybrid or remote work options.
A comfortable, modern office in
Kyiv
with generator and battery backup.
Competitive salary, medical insurance, and a supportive onboarding/trial period.
Team-building events, including parties, online activities, picnics, and more.
The opportunity to work in a
Top Employer company (DOU 2025)
.
Senior Machine Learning Engineer
to design and scale production ML systems that power real-time personalization and decision-making at scale.
In this role, you’ll own the
full ML lifecycle
—from transforming raw behavioral data into meaningful features, to deploying low-latency prediction APIs, to building the observability needed to keep models reliable in production.
This is a great opportunity for someone with strong
applied ML and MLOps expertise
who enjoys solving complex engineering challenges and building scalable, high-impact systems.
Your responsibilities will include:
Build and productionize ML models for ranking, personalization, and customer engagement.
Develop pipelines that transform behavioral, demographic, and contextual signals into online and offline features.
Design and deploy low-latency APIs and decision services for real-time decision-making.
Implement experimentation frameworks, including A/B testing and exploration-exploitation strategies.
Operationalize the ML lifecycle: automated training, CI/CD for models, artifact and feature versioning, and online/offline parity.
Build observability into ML systems by monitoring data quality, model drift, and decision outcomes, and triggering retraining when needed.
Establish closed feedback loops that connect decisions to business outcomes (e.g. conversions, engagement, fatigue signals such as unsubscribes).
Collaborate closely with product and engineering teams to balance personalization, compliance, and business value in real-world systems.
What we expect from you:
5+ years
of experience in applied ML engineering (recommendation systems, personalization, ranking, or advertising systems).
Strong proficiency in
Python or Go
, SQL, and modern ML frameworks such as
TensorFlow, PyTorch
, or similar.
Strong understanding of
MLOps best practices
, including CI/CD for ML, containerization (
Docker
), orchestration (
Kubernetes, Airflow, Kubeflow
), model registries, and monitoring frameworks.
Familiarity with cloud ML platforms such as
Vertex AI, SageMaker
, or similar, and data warehouses like
BigQuery, Snowflake, or Redshift
.
Experience deploying
real-time ML systems
, including low-latency serving, feature stores, and event-driven architectures.
Understanding of
multi-objective optimization
and trade-offs in personalization systems.
Comfort working cross-functionally in a dynamic startup environment with the
overlap
within USA time zone.
Strong spoken and written
English communication skills
.
Nice to have:
Experience in
martech, adtech, CRM
, or large-scale personalization platforms.
Exposure to
bandit algorithms, reinforcement learning, or causal inference
for adaptive decision-making.
Experience building systems serving
millions of users at scale
.
Hands-on experience with
Google Cloud Platform (GCP)
.
Familiarity with observability tools such as
Prometheus, Grafana, Evidently, WhyLabs,
or
Great Expectations
for monitoring data and model health.
What we offer:
Interesting projects and technical challenges that support both professional and personal growth.
A long-term project with stability and impact.
A flexible, results-oriented schedule with hybrid or remote work options.
A comfortable, modern office in
Kyiv
with generator and battery backup.
Competitive salary, medical insurance, and a supportive onboarding/trial period.
Team-building events, including parties, online activities, picnics, and more.
The opportunity to work in a
Top Employer company (DOU 2025)
.