Machine Learning Engineer

Senior ML Ops EngineerAbout the companyIt is an Artificial Intelligence company bringing the speed and insight of Applied AI tovisual assessment. Trained on millions of data points, our AI-powered solutions connecteveryone involved in insurance, repairs, and sales of homes and cars – helping people workfaster and smarter, while reducing friction and waste.Founded in 2014, it is now the AI tool of choice for world-leading insurance andautomotive companies. Our solutions unlock the potential of Applied AI to transform thewhole recovery ecosystem, from assessing damage and accelerating claims and repairs torecycling parts. They help make response to recovery up to ten times faster – even afterfull-scale disasters like floods and hurricanes.We're a diverse team, uniting individuals of over 40 different nationalities and from variedbackgrounds, with machine learning researchers and motor engineers collaborating togetheron a daily basis. We empower each team member to have tangible impact and grow theirown scope by intentionally building a culture centred around collaboration, transparency,autonomy and continuous learning.What you will doML foundations team focuses on building tools and services for our internal customer withinresearch, product, engineering and Operation specialists.We have 3 teams that tackle different aspects of this space, ML applications, Data operationsand ML Infrastructure. You'll be collaborating with peer teams and enhance, build and maintainthe ML infrastructure stack.We are looking for a Senior (Data|ML Ops) Engineer to build and support systems thatenable the core mission of the company - to make applied AI possible - by optimising theend-to-end Machine Learning life cycle. The vision of the ML Infrastructure is to enableresearchers to spend 80%+ of their time solving tricky ML problems rather than dealing withengineering/infra/ops challenges.You will help mature our ML and data platform to a world-class state. You will influence thescope and technical direction as well as champion best practices within the team. You havea 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 biggestimpact on your users. You will be a strong mentor, nurturing an encouraging and supportiveenvironment to enable the team to do their best work.The role:You'll play a key role in developing our ML & data platform from ground up, as part of a smallbut high-performing team. You will influence the scope and technical direction as well aschampion best practices within the team. You will continuously pursue clean code practicesand contribute towards overall platform architecture, collaborating with our other Engineeringand Product teams.You will:● Work with engineers, researchers and data scientists to build the next generation ofTractable's ML & data platform● Help identify and realise capabilities in our ML & data platform that massively speedup getting research to production across dataset & model management, modeltraining, model serving, labelling, data & ML pipeline orchestration and more● Support Research and Product Engineers with tools and processes to enable aseamless data flywheel● Deploy and continuously develop robust infrastructure, using best practices formanaging infrastructure-as-code● Solve cost and performance scalability challenges in both model training and modelserving● 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 roadmapTech Stack:We rely heavily on the following tools and technologies, but we are likely to explore newtechnologies / frameworks as we are building the platform from ground up. You don't need tohave prior experience in all of them, and we actively encourage diverse views on what thebest tools for the job are. We're just keen to know that you're willing to break things, fixthings, learn fast and help build a great team that is capable of building a platform thatdelights our customers.● Main Infrastructure: AWS (EC2, S3, MSK, Lambda, StepFunctions, Glue, IAM,Cognito, Systems Manager, CloudWatch, SQS, Route 53, Sagemaker), ApacheKafka (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, AWSGlue● Main ML stack: Triton, TFServing, KServe, AWS Sagemaker, AWS Lambda, AWSMSK, sync/async APIs, Weights & Biases, Tensorflow, Pytorch, dvc, Dagster/Flyte,StreamlitWhat you need to be successful (ML OPS ENGINEER):A strong ML Engineer who is passionate about building platforms that massively reduce leadtime from bringing Machine Learning research to production. You have a solid background incore software engineering principles and a good understanding of the difficulties faced bydata scientists. A few things we are particularly interested in seeing from you:● Have experience in building and managing end-to-end machine learning pipelines, frommodel training to deployment.Experienced in managing and constructing complete machinelearning 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, highlycross-functional environment● 2+ years of experience in building scalable Machine Learning systems● Have experience building and/or managing scalable data infrastructure (dataingestion, data lake, data warehouse, data orchestration)● Strong programming experience, from self-contained algorithms to complex objectmodelling 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 comparetrade-offs (distributed systems experience a plus)● Experience building robust, intuitive tooling to support internal users (e.g. commonML 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 scalehighly desirableWhat's 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