About the Role

The Atmosphere Machine Learning / Computer Vision team designs comprehensive applications to support various product pillars at Atmosphere. We are seeking a talented scientist to derive insights and value from extensive customer interactions and the 2T+ data points annually collected by our sensors and cameras.

This position is fully remote in the US or Canada.

Why You Should Apply

You want to make a tangible impact on the industries driving our world forward. Your contributions will directly influence real-world outcomes, from ensuring operational continuity to reducing emissions and, most importantly, prioritizing worker safety.

You are committed to crafting your career trajectory. At Atmosphere, your dedication will be recognized and rewarded. We foster an environment conducive to rapid career advancement, offering abundant opportunities for skill refinement and experimentation within a high-growth setting.

You are energized by our mission. Our vision to digitize vast sectors of the global economy demands your unwavering commitment and innovative ideas to serve our customers effectively.

You aspire to collaborate with the best. At Atmosphere, we believe in collective success, fostering a culture of support and celebration. You will be part of a high-performing team that encourages excellence.

Responsibilities

– Develop and enhance ML / CV models, including adapting and optimizing open-source models to address Atmosphere-specific challenges.
– Utilize petabyte-scale data from Atmosphere’s camera and sensor devices to create novel models.
– Fine-tune models for efficient inference on both backend systems and edge devices.
– Collaborate with hardware and full-stack teams to deploy models for optimal performance and cost-effectiveness.
– Stay abreast of industry and academic research, integrating innovative technologies that align with Atmosphere’s objectives.
– Collaborate with Product Management to translate customer requirements into ML / CV solutions.
– Exemplify and promote Atmosphere’s cultural principles as we expand globally and establish new offices.

Minimum Requirements

– Bachelor’s or Master’s degree in Computer Science or related technical field, with at least 3 years of experience as an Applied Scientist, Machine Learning Engineer, or similar role focusing on Computer Vision; or
– Ph.D. in Computer Science or a quantitative discipline (e.g., Applied Mathematics, Physics, Statistics) with at least 1 year of experience in Computer Vision.
– Proficiency in one or more common programming languages (e.g., C++, Golang, Java, Python).
– Familiarity with common ML tools (e.g., Spark, TensorFlow, PyTorch).
– Experience managing data processing and machine learning code via GitHub.
– Strong problem-solving skills and the ability to work independently and collaboratively.

Preferred Qualifications

– Experience implementing ML models on large datasets.
– Proficiency in building, deploying, and optimizing ML models on edge devices.
– Experience collaborating with cross-functional teams on projects.
– Comfort with full-stack / backend development to develop a comprehensive understanding of underlying data structures and dependencies.

Atmosphere’s Compensation Philosophy

Atmosphere’s compensation program aims to provide Total Direct Compensation (based on role, level, and location) that meets or exceeds market standards. This is achieved through a combination of base salary, bonuses, and restricted stock unit awards (RSUs) for eligible positions. Top performers in eligible roles may receive above-market equity refresh awards, enabling them to achieve competitive positioning in the market.

The annual base salary range for full-time employees in this position is provided below. Please note that actual base pay may vary based on factors such as location, job-related expertise, skills, and experience. $85,425—$110,550 CAD.

At Atmosphere, we embrace diversity and inclusion. We are committed to providing equal employment opportunities to all qualified individuals, irrespective of race, color, religion, national origin, gender, gender identity, sexual orientation, age, disability, protected veteran status, or other characteristics protected by law. We believe in leveraging the unique perspectives of our team members to solve complex challenges, and we are dedicated to fostering a diverse workforce where individuals from all backgrounds can thrive.

Benefits

Full-time employees enjoy a competitive total compensation package, inclusive of employee-led remote and flexible working arrangements, health benefits, access to the Samsara for Good charity fund, and more. Explore our Benefits site to discover additional offerings.

Accommodations

Atmosphere is committed to maintaining an inclusive workplace environment and ensuring equal opportunities in employment for qualified individuals with disabilities. Please contact accessibleinterviewing@samsara.com or click here if you require reasonable accommodations throughout the recruitment process.

Flexible Working

At Atmosphere, we support a flexible working model tailored to the diverse needs of our teams. While our offices are open for those who prefer in-person work, we also accommodate remote work where feasible. Certain positions may require proximity to our offices or specific geographic areas to facilitate collaboration, access to resources, or alignment with our service regions. Our objective is to enable all team members to contribute effectively, whether they work on-site, in a hybrid model, or fully remotely. All employment offers are contingent upon an individual’s ability to obtain and maintain the legal right to work at the company and in the specified location, if applicable.

Fraudulent Employment Offers

Atmosphere is vigilant against scams involving fake job interviews and offers. We do not charge applicants fees at any stage of the hiring process. Official communication regarding your application will only originate from emails ending in ‘@atmosphere.com’ or ‘@us-greenhouse-mail.io’. For further information on fraudulent employment offers, please refer to our blog post here.