Machine Learning Engineer

Job Description

Senior ML Ops Engineer

About the company

It is an Artificial Intelligence company bringing the speed and insight of Applied AI to

visual assessment. Trained on millions of data points, our AI-powered solutions connect

everyone involved in insurance, repairs, and sales of homes and cars helping people work

faster and smarter, while reducing friction and waste.

Founded in 2014, it is now the AI tool of choice for world-leading insurance and

automotive companies. Our solutions unlock the potential of Applied AI to transform the

whole recovery ecosystem, from assessing damage and accelerating claims and repairs to

recycling parts. They help make response to recovery up to ten times faster even after

full-scale disasters like floods and hurricanes.

We&aposre a diverse team, uniting individuals of over 40 different nationalities and from varied

backgrounds, with machine learning researchers and motor engineers collaborating together

on a daily basis. We empower each team member to have tangible impact and grow their

own scope by intentionally building a culture centred around collaboration, transparency,

autonomy and continuous learning.

What you will do

ML foundations team focuses on building tools and services for our internal customer within

research, product, engineering and Operation specialists.

We have 3 teams that tackle different aspects of this space, ML applications, Data operations

and ML Infrastructure. You&aposll be collaborating with peer teams and enhance, build and maintain

the ML infrastructure stack.

We are looking for a Senior [Data|ML Ops] Engineer to build and support systems that

enable the core mission of the company - to make applied AI possible - by optimising the

end-to-end Machine Learning life cycle. The vision of the ML Infrastructure is to enable

researchers to spend 80%+ of their time solving tricky ML problems rather than dealing with

engineering/infra/ops challenges.

You will help mature our ML and data platform to a world-class state. You will influence the

scope and technical direction as well as champion best practices within the team. You have

a relentless focus on user experience (researchers, data scientists and product engineers)

and you care deeply about what your team is building to make sure it will have the biggest

impact on your users. You will be a strong mentor, nurturing an encouraging and supportive

environment to enable the team to do their best work.

The role:

You&aposll play a key role in developing our ML & data platform from ground up, as part of a small

but high-performing team. You will influence the scope and technical direction as well as

champion best practices within the team. You will continuously pursue clean code practices

and contribute towards overall platform architecture, collaborating with our other Engineering

and Product teams.

You will:

Work with engineers, researchers and data scientists to build the next generation of

Tractables ML & data platform

Help identify and realise capabilities in our ML & data platform that massively speed

up getting research to production across dataset & model management, model

training, model serving, labelling, data & ML pipeline orchestration and more

Support Research and Product Engineers with tools and processes to enable a

seamless data flywheel

Deploy and continuously develop robust infrastructure, using best practices for

managing infrastructure-as-code

Solve cost and performance scalability challenges in both model training and model

serving

Run, monitor and maintain business-critical, production systems

Adopt open-source technologies to best leverage our in-house resources

Promote engineering best practices throughout the team

Suggest, collect and synthesise requirements to create an effective feature roadmap

Tech Stack:

We rely heavily on the following tools and technologies, but we are likely to explore new

technologies / frameworks as we are building the platform from ground up. You don&apost need to

have prior experience in all of them, and we actively encourage diverse views on what the

best tools for the job are. Were just keen to know that you&aposre willing to break things, fix

things, learn fast and help build a great team that is capable of building a platform that

delights our customers.

Main Infrastructure: AWS (EC2, S3, MSK, Lambda, StepFunctions, Glue, IAM,

Cognito, Systems Manager, CloudWatch, SQS, Route 53, Sagemaker), Apache

Kafka (AWS MSK), Kubernetes, Datadog (Metrics, Logs, Synthetics), Pagerduty,

Loki, Elastic Search

Main CI/CD: Terraform, Docker, Harness

Main Databases: Postgres / RDS, Redis, DynamoDB

Main Languages: Python, Node + Typescript, SQL (Postgres)

Main Data stack: AWS MSK, AWS Lambda, AWS Redshift, dbt, Airflow, Airbyte, AWS

Glue

Main ML stack: Triton, TFServing, KServe, AWS Sagemaker, AWS Lambda, AWS

MSK, sync/async APIs, Weights & Biases, Tensorflow, Pytorch, dvc, Dagster/Flyte,

Streamlit

What you need to be successful [ML OPS ENGINEER]:

A strong ML Engineer who is passionate about building platforms that massively reduce lead

time from bringing Machine Learning research to production. You have a solid background in

core software engineering principles and a good understanding of the difficulties faced by

data scientists. A few things we are particularly interested in seeing from you:

Have experience in building and managing end-to-end machine learning pipelines, from

model training to deployment.Experienced in managing and constructing complete machine

learning pipelines, spanning from model training to deployment.

Great communication skills and a collaborative mindset

An ability to catalyse both process and technical change in a complex, highly

cross-functional environment

2+ years of experience in building scalable Machine Learning systems

Have experience building and/or managing scalable data infrastructure (data

ingestion, data lake, data warehouse, data orchestration)

Strong programming experience, from self-contained algorithms to complex object

modelling design

Worked with Python in a professional environment for 2+ years

Experience working with and scaling model training across GPU clusters

Experience in building data pipelines and managing data infrastructure

Experience deploying and managing infrastructure-as-code

Able to design scalable, robust, fault-tolerant system architecture and compare

trade-offs (distributed systems experience a plus)

Experience building robust, intuitive tooling to support internal users (e.g. common

ML libraries, CLIs etc.)

Experience deploying and managing infrastructure-as-code, preferably via AWS CDK

Numerical computing experience

Cares about team practices / pairing / advocate of CICD

Basic ML knowledge, with experience in training computer vision models at scale

highly desirable

Whats in it for you

Competitive salary

6 month salary reviews

Equity

Pension scheme

Bupa private healthcare (full coverage)

Flexible hours & WFH/hybrid setups

Learning and Development budget

Competitive maternity + paternity leave

Daily office snacks & soft drinks

Regular company office events such as Games Nights, Movie Nights, Lunch &

Learns, Monthly Brunch and more

---