Senior Machine Learning Engineer, Trellis

Real-Time Bidding

Salary not provided

+ Bonuses + Equity

SQL

GCP

Python

Tensorflow

Scikit-Learn

PyTorch

BigQuery

Senior and Expert level

Remote from Canada, US

P&C insurance data services

Be an early applicant

P&C insurance data services

21-100 employees

FintechB2BEnterpriseInsuranceAPI

Be an early applicant

Salary not provided

+ Bonuses + Equity

SQL

GCP

Python

Tensorflow

Scikit-Learn

PyTorch

BigQuery

Senior and Expert level

Remote from Canada, US

21-100 employees

FintechB2BEnterpriseInsuranceAPI

Company mission

To build industry-leading solutions that make insurance easy for everyone, from consumers to partners to insurers.

Job

Company

Role

Who you are

5+ years of ML engineering experience, including end-to-end deployment in production environments
Proficiency in Python and ML frameworks (TensorFlow, PyTorch, or Scikit-learn)
Experience with cloud ML platforms, preferably GCP (Vertex AI, Dataflow, Pub/Sub, BigQuery)
Familiarity with MLOps practices (CI/CD for ML, monitoring, reproducibility)
Strong data skills: SQL, feature engineering, and big data processing
A pragmatic mindset: delivering fast, measurable improvements while balancing complexity and scale

What the job involves

Trellis is hiring a Senior Machine Learning Engineer to advance our real-time bidding (RTB) systems
You will design, implement, and scale ML models that directly power high-volume bidding decisions across millions of daily ad auctions
This includes end-to-end ownership of the ML lifecycle: data exploration, feature engineering, model training, deployment, and monitoring in production
You’ll leverage Google Cloud Platform services (BigQuery, Vertex AI, Dataflow, Pub/Sub) and Python-based ML frameworks (TensorFlow, PyTorch, Scikit-learn) to deliver measurable improvements in bidding efficiency and ROI
This is a fully remote position, based in the US or Canada, and will report directly into the Head of RTB, Thomas Boquet
Research, design, and implement advanced ML models for conversion rate prediction, value estimation, and pacing
Build robust feature pipelines with BigQuery and Dataflow, ensuring low-latency inference at scale
Deploy, monitor, and continuously improve models in production using Vertex AI
Develop and maintain MLOps frameworks to automate training, evaluation, and deployment
Partner with backend and distributed systems engineers to integrate models into high-throughput bidding services
Contribute to experimentation frameworks and AB testing of ML-driven bidding strategies

Our take

Changing insurance providers can be a daunting and overwhelming process. Its a complex and lengthy undertaking, resulting in many people sticking to the same provider, even if it doesn't best suit their needs.

Recognizing the need for a better solution is Trellis, that delivers an API platform for consumers to compare insurance policies side-by-side. It also partners with digital banks and financial institutions to offer new insurance options to their existing customers - a feature which allows it to stand out from the competition.

Trellis has seen exponential growth since founding in 2019, and has secured strong funding from the likes of Amex Ventures. The company is now in a prime position to accelerate its offerings and further innovate the insurance market.

Freddie

Company Specialist at Welcome to the Jungle

Company

Funding (2 rounds)

May 2022

$5m

EARLY VC

Jun 2021

$10m

SERIES A

Total funding: $15m

Company benefits

Bonuses and equity opportunities
Unlimited vacation time
100% employer-paid insurance for employee, 60% for dependents
401K plan
Annual in-person retreats

Company HQ

SoMa, San Francisco, CA

Leadership

Daniel Demetri

(Founder & CEO)

Previously co-founded the insurance company, Doma. Also worked as a Product Manager at Google as well as a Product Lead/Head of Product Management at Earnest.

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