Embedded ML Engineer – Vision

Reposted 6 Days Ago
San Francisco, CA
In-Office
175K-280K Annually
Senior level
Information Technology
The Role
The Embedded ML Engineer will develop computer vision algorithms for low-power devices, collaborating with multiple teams and managing end-to-end ML workflows.
Summary Generated by Built In

About Sesame

Sesame believes in a future where computers are lifelike - with the ability to see, hear, and collaborate with us in ways that feel natural and human. With this vision, we're designing a new kind of computer, focused on making voice companions part of our daily lives. Our team brings together founders from Oculus and Ubiquity6, alongside proven leaders from Meta, Google, and Apple, with deep expertise spanning hardware and software. Join us in shaping a future where computers truly come alive.

About the Role

We are looking for an engineer who thrives at the boundary of computer vision and embedded machine learning. This role is about pushing the limits of what’s possible in vision-based interaction on resource-constrained devices. You’ll design and deploy algorithms that turn raw sensor data into seamless user experiences, balancing innovation in computer vision with the practical realities of ultra-low-power, wearable hardware. Working cross-functionally with research, hardware, firmware, and product teams, you’ll take ideas from the whiteboard all the way to production devices in customers’ hands.

Responsibilities:

  • Design, train, and deploy algorithms for computer vision on low-power embedded hardware.

  • Adapt and compress larger ML models to fit power, memory, and latency constraints of real-time wearable systems.

  • Own the full ML development cycle: system design, data collection & curation, synthetic data generation, model training & evaluation, and on-device optimization.

  • Collaborate closely with electrical, mechanical, firmware, and product teams to co-design algorithms and hardware in tandem.

  • Pick promising approaches from the literature to bet on, and create new approaches where necessary to achieve our unique goals.

Required Qualifications:

  • 5 years of experience in Software Engineering, ML Research, or related fields.

  • Experience working with a high degree of autonomy in ambiguous environments.

  • Proven experience developing and deploying computer vision ML models on resource-constrained or embedded devices.

  • Proficiency in C/C++ as well as Python, with strong experience in deep learning frameworks such as PyTorch, TensorFlow, or Jax.

  • Hands-on experience with end-to-end ML workflows, from data capture to on-device deployment.

  • Strong grasp of signal processing, vision-based feature extraction, and/or time-series analysis.

  • Excellent communication skills and the ability to work collaboratively across disciplines.

  • Bachelor’s degree or higher in computer science, electrical engineering, computer vision, machine learning, or a related field.

Preferred Qualifications:

  • Experience with wearables, cameras, IMUs, or tactile/force sensors.

  • Familiarity with synthetic data generation and augmentation techniques.

  • Experience in a startup or fast-moving product environment.

  • Experience deploying models in products.

  • Track record of optimizing algorithms for power, latency, and memory footprint.

  • Master’s / Ph.D. in a relevant field.

Sesame is committed to a workplace where everyone feels valued, respected, and empowered. We welcome all qualified applicants, embracing diversity in race, gender, identity, orientation, ability, and more. We provide reasonable accommodations for applicants with disabilities—contact [email protected] for assistance.

Full-time Employee Benefits: 

  • 401k matching

  • 100% employer-paid health, vision, and dental benefits 

  • Unlimited PTO and sick time 

  • Flexible spending account matching (medical FSA) 

Benefits do not apply to contingent/contract workers

Top Skills

C/C++
Jax
Python
PyTorch
TensorFlow
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The Company
HQ: San Francisco, CA
55 Employees
Year Founded: 2023

What We Do

Bringing the computer to life

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