Senior Software Engineer, Algorithms + ML

ALSO•Palo Alto, CA

About The Position

We are seeking a senior, product-minded Software Engineer with deep experience in algorithms, machine learning, and edge AI. In this role, you will take ambiguous customer and product needs, model the underlying technical problem, and translate solutions into reliable, production-ready code that runs across mobile, embedded, and cloud environments. You will partner closely with Product, Design, Embedded, Mobile, Cloud, Data, Manufacturing, and Systems Engineering teams to make smart architectural decisions about where algorithms should run, how they should be deployed, how their performance should be measured, and how they improve over time. This role is ideal for someone who can move fluidly from problem definition to modeling, experimentation, implementation, deployment, telemetry, and iteration. You should be comfortable balancing algorithmic quality with real-world product constraints such as latency, battery life, compute, bandwidth, reliability, and manufacturability.

Requirements

5+ years of professional experience building algorithms, ML systems, or production software for mobile, embedded, cloud, or connected products.
A track record of shipping algorithms or ML models into production, ideally on resource-constrained devices, mobile applications, embedded systems, or cloud-connected products.
Strong hands-on programming skills in Python and C/C++, with the ability to write production-quality, testable, and maintainable code.
Data manipulation with SQL and knowledge of Data Warehousing solutions like DataBricks or Snowflake.
Practical knowledge of algorithm development, model building, or signal-processing workflows using tools such as MATLAB, NumPy, SciPy, PyTorch, TensorFlow, or similar frameworks.
Familiarity deploying models or algorithms to edge environments using tools such as TensorFlow Lite, Edge Impulse, PyTorch Mobile, ExecuTorch, or comparable edge deployment frameworks.
Demonstrated ability to optimize deployed algorithms against real-world constraints such as latency, battery life, compute usage, memory footprint, bandwidth, reliability, and product behavior.
Comfort working with telemetry, logs, device data, and production performance data to diagnose issues, measure outcomes, identify outliers, and improve algorithm performance over time.
Hands-on involvement with data schemas, data pipelines, or edge-to-cloud telemetry flows that support model validation, monitoring, retraining, diagnostics, or experimentation.
Working knowledge of experimentation methods such as A/B testing, shadow testing, offline evaluation, simulation, synthetic data generation, and production monitoring.
Familiarity with CI/CD pipelines, Docker, automated testing, and MLOps or model deployment workflows.
Domain knowledge in one or more areas such as mobility, IoT, automotive, fitness, wearables, robotics, consumer electronics, or connected devices.
The ability to collaborate with Embedded, Mobile, Cloud, Data, Product, Design, Manufacturing, and Systems Engineering teams to make practical technical trade-offs.
Clear communication skills, including the ability to explain algorithm behavior, performance trends, trade-offs, risks, and recommendations to technical and non-technical stakeholders.

Nice To Haves

Exposure to real-time operating systems, embedded Linux, iOS, Android, hardware-in-the-loop systems, simulation environments, factory production, or manufacturing validation workflows is a plus.

Responsibilities

Design, develop, deploy, and iterate on algorithms and ML models across mobile, embedded, and cloud platforms.
Partner with Product Managers, Designers, and Engineering stakeholders to translate customer needs into clear algorithmic requirements.
Work with platform and domain experts to deploy algorithms into production and monitor their behavior in real-world environments.
Analyze model and algorithm performance using experimentation, A/B testing, shadow testing, telemetry analysis, and offline evaluation.
Design and maintain input/output data schemas to support efficient edge-to-cloud telemetry, diagnostics, model retraining, and continuous improvement.
Optimize deployed algorithms for latency, battery usage, communication efficiency, robustness, and intended product behavior.
Identify and investigate outliers, failure modes, regressions, and edge cases in production data.
Use real-world telemetry data and generate synthetic data where needed for training, testing, validation, and simulation.
Build processes to monitor model performance, data drift, model drift, and concept drift over time.
Collaborate with Cloud and Data Engineering teams on data pipelines that support model development, validation, deployment, and retraining.
Support algorithms used in customer-facing products as well as factory, manufacturing, and validation workflows.
Manage technical priorities, project timelines, and trade-offs in a fast-moving product development environment.
Communicate technical findings, performance trends, and improvement plans clearly to technical and non-technical stakeholders.