The Simulator team builds state of the art simulations of realistic environments for the testing and training of the Waymo driver. We use machine learning extensively to model the real world, including but not limited to, realistic agents (vehicles, pedestrians, cyclists, motorcyclists etc.),  roads, traffic control systems, weather etc. To increase the fidelity and steerability of the simulations, we employ large foundation models, trained on our massive datasets that allow us to quickly setup and rollout various scenarios to subject our driver to.

We are planning to set up a team in London UK to work closely with the teams in MTV and Oxford to build these foundation models out and to integrate them into various evaluation and training products. We are seeking research engineers to work on these exciting problems.

In this research engineering role, you'll:

• Be part of a world class research engineering team to advance the state of the art of ultra realistic AV simulations using foundation models
• Collaborate with teams in Waymo Oxford to use large models to improve sim realism
• Design experiments that push the frontiers of AV simulations
• Develop metrics that measure the realism of simulated worlds
• Train and evaluate large models and integrate them into the simulator and its downstream applications
• Help hire outstanding research engineers from diverse backgrounds
• Be a part of a high performing, collaborative research engineering team that takes research ideas and productionizes them

Preferred qualifications

• Strong background and experience in applied Deep Learning
• Strong coding and design skills
• Experience in solving complex production problems using state of the art ML techniques
• Experience in taking research to production
• Expertise in Data Analysis or Data Science
• Experience in AV a plus

About the role:

The Samsara ML Experience team builds end-to-end ML applications to power different product pillars at Samsara. As a Senior Machine Learning Engineer II, you will be responsible for developing ML solutions to increase the safety, efficiency and sustainability of the physical operations. You will work closely with various engineering teams across ML, full-stack, firmware as well as cross functional partners to deliver core infrastructure, services, and optimizations.

This role is open to candidates residing in the Canada and US except the San Francisco Bay Area (125 mi. radius from 1 De Haro St, San Francisco) and NYC Metro Area (50 mi. radius from 131 W 55th St, New York).

You should apply if:

• You want to impact the industries that run our world: The software, firmware, and hardware you build will result in real-world impact—helping to keep the lights on, get food into grocery stores, and most importantly, ensure workers return home safely.
• You want to build for scale: With over 2.3 million IoT devices deployed to our global customers, you will work on a range of new and mature technologies driving scalable innovation for customers across industries driving the world's physical operations.
• You are a life-long learner: We have ambitious goals. Every Samsarian has a growth mindset as we work with a wide range of technologies, challenges, and customers that push us to learn on the go.
• You believe customers are more than a number:Samsara engineers enjoy a rare closeness to the end user and you will have the opportunity to participate in customer interviews, collaborate with customer success and product managers, and use metrics to ensure our work is translating into better customer outcomes.
• You are a team player: Working on our Samsara Engineering teams requires a mix of independent effort and collaboration. Motivated by our mission, we’re all racing toward our connected operations vision, and we intend to win—together.

Click hereto learn about what we value at Samsara. 

In this role, you will:

• Design and implement scalable machine learning infrastructure using Ray to support model training, deployment, and inference at scale
• Leverage Kubernetes for orchestration of containerized applications, ensuring seamless deployment, scaling, and management of ML models and associated services
• Develop and maintain CI/CD pipelines for automated testing, deployment, and management of ML applications and infrastructure
• Implement robust monitoring, logging, and alerting systems to ensure high availability, performance, and security of the ML platform
• Collaborate with data scientists and ML engineers to optimize data pipelines and model performance
• Stay abreast of the latest advancements in machine learning technologies and infrastructure, and advocate for the adoption of best practices and new technologies within the team
• Provide DevOps/SRE support for the ML platform, including incident response, performance tuning, and disaster recovery planning
• Champion, role model, and embed Samsara’s cultural principles (Focus on Customer Success, Build for the Long Term, Adopt a Growth Mindset, Be Inclusive, Win as a Team) as we scale globally and across new offices

Minimum requirements for the role:

• BS or  MS in Computer Science or other relevant field
• 6+ years experience as an Machine Learning Engineer or similar role
• Strong proficiency in one or more common languages (e.g., C++, Golang, Java, Python, Scala) 
• Proficiency with common ML tools (e.g., Spark, TensorFlow, PyTorch)
• Experience with monitoring and logging tools (e.g., Prometheus, Grafana, ELK stack) and security best practices for ML platforms

An ideal candidate also has:

• Ph.D. in Computer Science or quantitative discipline (e.g., Applied Math, Physics, Statistics)
• Strong experience with Ray for distributed machine learning, Kubernetes for container orchestration, and Docker for containerization
• Solid understanding of DevOps and SRE principles, including experience with CI/CD tools (e.g., Jenkins, GitLab CI), infrastructure as code (e.g., Terraform, Ansible), and cloud services (AWS, GCP, Azure).

Why you'll love this role

Our AI/ML Team is on a mission to build the next-generation e-commerce platform for the next generation customer. We build world-class, innovative experiences and products that give our users access to the world’s most coveted products and unlock economic opportunity by turning reselling into a business for anyone. Our team uses cutting edge technologies that handle massive scale globally. We’re an internet-native, cloud-native company from day 1 - you won’t find legacy technology here. If you’re a curious leader who loves solving problems, wearing multiple hats, and learning new things, join us!

In the Search & Recommendation team, we work together to productionalize custom machine-learning models that can drive product vision and customer impact at scale. We are looking for MLE who are product driven, and are passionate about making ML innovations in areas such as; Ranking, Optimization, Natural Language Processing, Information Retrieval, Graph Learning, Reinforcement Learning to help improve the StockX buyer/seller experience!

Example Projects:

• Develop embeddings to collect salient signals of our customers, product, and user interactions.
• Extract real-time signals and multi-modality data (i.e, content and image) from our 5M+ product catalog images and 1M+ listings. Understand semantic content, aesthetic style, materials for retrieval, ranking and optimization.
• Build a real-time, in-session personalization recommendation system.
• Implement and compare supervised learning models (i.e, LR, GBDT, and DNNs) or ensembles of models, to improve metrics, often with multiple contending objectives (i.e, relevance, degree of personalization, average value of orders, repeated frequencies/purchases).
• Develop models with custom architecture or objective functions that target StockX-specific problems, such as recommendation system, personalized search, revenue optimization, seller fairness, seasonality, etc.
• Develop brand-new learning frameworks for query suggestions to understand buyer experience.

What you'll do

• Apply the latest advances in deep learning and machine learning to improve buyer and seller experiences on StockX.
• Prototype, optimize, and productionize large-scale ML models that help deliver key results in search experience.
• Conduct A/B experiments to validate ML models and pipelines.
• Work closely with product managers, Data scientists/engineers, full-stack engineers, and designers on product teams to deliver content to tens of millions of users.

About you

• Experience with object-oriented or functional software development.
• Experience working with AWS or other cloud providers.
• Experience with big data platforms like Spark or Databricks.
• Experience with machine learning libraries such as TensorFlow, PyTorch, or MXNet.
• You have dealt with data exploration, analysis, and feature engineering.
• You have relentlessly high standards for the products you deliver.
• Work effectively in an agile development process.
• You have a postgraduate degree in Computer Science or related engineering fields plus 1+ machine learning experience, or 3+ years of practical machine learning experience.
• Experience with Kubernetes and Docker for productionalizing models.
• You have experience in building machine learning systems at scale.
• You have experience in using AWS Cloud Platform and/or OpenSearch.
• You have experience in building production search, recommendations, advertising, or general e-commerce systems.

Pursuant to the San Francisco Fair Chance Ordinance, Los Angeles Fair Chance Initiative for Hiring Ordinance, and any other state or local hiring regulations, we will consider for employment any qualified applicant, including those with arrest and conviction records, in a manner consistent with the applicable regulation.

Pursuant to the various pay transparency laws/acts, the base salary range is $120,000 to $140,000 plus opportunities for benefits (e.g., medical, dental), equity and discretionary bonuses. Compensation is dependent on geography and may vary.

Who You’ll be Joining:

The ecobee Machine Learning team researches, builds and deploys, at scale, the engine that powers the heart of ecobee’s smart devices. Our engineers work with modern technologies and help build solutions to make our ecosystem of products more intelligent and more capable of responding to our customer’s individual needs than ever before. We're looking for highly creative, results-oriented, and skilled Staff Machine Learning Engineers to help on a range of these challenges. You will realize that the sky is the limit to your growth–all while having fun!

Our team is working on several exciting areas such as Smart Home Monitoring, Data-driven Home Energy Management and ML-driven Customer Engagement.

How You’ll Make an Impact:

Working across multiple teams within Machine Learning, you’ll use your expertise in machine learning to support and advance various projects while also collaborating closely with members of our Product and Business Intelligence teams on missions ranging from personalization, recommendations, energy efficiency, home security, and building a cleaner energy grid.

You’ll contribute directly to the development of our machine learning infrastructure, iterating quickly, running and scaling experiments with data sets containing hundreds of billions of data points, and rapidly shipping products on both the cloud and the edge.

As a Staff Machine Learning Engineer at ecobee, you will;

• Lead a team in translating business use cases into concrete software problems, applying innovative technologies and consistently balances algorithmic elegance with engineering considerations such as scale and maintenance and overhead.
• Lead exploration, design and execution of ML models and frameworks that deliver value to our users.
• Collaborate closely with technical and non-technical stakeholders, including business partners, engineering and analytics teams to take services from inception to production.
• Manage the full machine learning development lifecycle from problem framing, data wrangling, model development and fine-tuning all the way to productionization, experimentation, and maintenance.
• Determine the feasibility of initiatives through quick prototyping and with respect to performance, quality, time, and cost.
• Bring cutting-edge research and industry knowledge into the team around natural language processing (NLP), computer vision, machine learning, generative AI, and related areas.
• Provide technical mentorship and coaching to the team members.
• Takes ownership for finding creative solutions that move projects forward, by continuously researching and staying up-to-date with the latest machine learning research, translating new techniques and algorithms into innovative solutions.

What You’ll Bring to the Table

We’ve built the following list as a guideline for some of the skills and interests of our development team — but we strive to build our team with members from a diverse background and skillset, so if any combination of these applies to you, we’ve love to chat!

• Graduate degree (Masters/PhD) in Statistics, Mathematics, Computer Science or another quantitative field.
• 6+ years’ of professional experience applying machine learning to multiple projects aimed at solving real-world problems.
• 6+ years’ of professional experience with manipulating data sets, building statistical models, and productionizing machine learning solutions using state-of-the-art techniques
• Strong knowledge of machine learning frameworks (pytorch, tensorflow or Keras), libraries (NumPy and scikit-learn), data structures, data modeling, and software architecture.
• Demonstrable experience developing and training production-ready machine learning models including transformer models, reinforcement learning, neural networks, model training in an area such as classifiers, recommenders, vision, and speech.
• Experience working with data at the scale of 1TB or more, leveraging big data processing frameworks like Spark and Google Cloud Dataflow.
• Experience with software engineering, ML-Ops deployment and infrastructure best practices.
• Proven software engineering skills across multiple languages such as Python, C/C++, and the accompanying frameworks and libraries used in machine learning packages.
• Proven ability to lead cross-functional projects to successful conclusions using strong problem-solving skills and the ability to communicate complex concepts to technical and non-technical stakeholders.
• Expertise working in environments that leverage Scrum and Agile.

What happens after you apply?

Application Review. It will happen. By an actual person in Talent Acquisition. We get upwards of 100+ application for some roles, it can take a few days, but every applicant can expect a note regarding their application status.

Interview Process (3 Rounds)

• Round 1: A 45-minute phone call with a member of Talent Acquisition.
• Round 2: A 45-minute virtual meeting with a Senior Machine Learning Manager. This will be a technical interview in the form of a Q&A.
• Completion of a machine learning take-home challenge.
• Round 3 is broken into two parts;
  • Part 1: A 1-hour virtual meeting with a pair of senior members of our Machine Learning team. The interview would review the breadth and depth of data and ML knowledge.  
  • Part 2: A 45-minute virtual meeting our VP of Data Science, and a Staff Product Manager for a Q&A on machine learning in a product-context.