Dyna Robotics

Senior Machine Learning Engineer

Dyna Robotics, Redwood City

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Dyna Robotics is at the forefront of revolutionizing robotic manipulation with cutting-edge foundation models. Our mission is to empower businesses by automating repetitive, stationary tasks with affordable, intelligent robotic arms. Leveraging the latest advancements in foundation models, we're driving the future of general-purpose robotics—one manipulation skill at a time.

Dyna Robotics was founded by industry leaders who previously achieved a $350 million exit in grocery deep tech as well as top robotics researchers from DeepMind and Nvidia. Our team blends world-class research, engineering, and product innovation to drive the future of robotic manipulation. With sizable funding already in place, we're positioned to redefine the landscape of robotic automation. Join us to shape the next frontier of AI-driven robotics.

The Position

We are hiring a Senior Machine Learning Engineer who’s passionate about building and deploying cutting-edge AI models that work reliably in the real world. In this role, you will own production research pipelines end-to-end—by training advanced models, deeply analyzing their behavior, and driving improvements from data collection to data processing to final deployment.

Your work will focus on identifying data gaps, debugging model failures, analyzing edge cases, and continuously improving model behavior through iterative feedback loops between observation and action, as well as designing systems to automate this feedback loop.

You’ll bring both strong model training skills and a sharp eye for debugging. You’ll dig into training logs, analyze unexpected outputs, and resolve subtle implementation issues to improve overall system performance. You’ll also help translate ambiguous, real-world challenges into clear, solvable research and engineering problems that drive progress in production.

Key Responsibilities

Train and fine-tune vision-language models (VLMs) and other large deep learning models for robotic manipulation tasks.
Develop and iterate on reinforcement learning models for closed-loop control and long-horizon decision making.
Analyze training results, detect performance bottlenecks, and identify failure modes across datasets and models.
Investigate and resolve issues related to training stability, model regressions, or suboptimal architecture/config choices.
Collaborate with data and robotics teams to refine data collection, labeling, and filtering strategies based on model needs.
Run structured experiments and ablations to isolate root causes and inform iterative improvements.
Build tools and dashboards for tracking metrics, surfacing anomalies, and visualizing model performance.
Translate practical product needs or robot behaviors into well-scoped research or engineering problems.

Qualifications

5+ years of experience in applied machine learning, deep learning, or AI research.
Strong foundation in machine learning and deep learning, especially experience with Transformer architectures and representation learning.
Familiarity with deep reinforcement learning
Proven ability to debug, optimize, and improve deep learning models in production or research settings.
Track record of turning ambiguous real-world issues into actionable research/engineering projects.
Understands how to write scalable, modular, and maintainable research code in collaborative codebases.
Proficiency in Python and deep learning frameworks such as PyTorch, TensorFlow, or JAX.
Comfortable with data analysis tools and workflows (e.g., NumPy, Pandas; Weights & Biases, or custom metric tracking).
Methodical, detail-oriented approach to experimentation, logging, and performance tuning.
Bonus: Experience working with robotics systems (ROS, Isaac Sim, MuJoCo) or production inference/debugging infrastructure.
Competitive salary and equity in a seed-stage venture-backed startup
Comprehensive health, dental, and vision insurance
Daily catered lunches and dinner with a fully stocked kitchen
Professional growth and development through training, mentorship, and challenging projects

Compensation : The base salary range for this full-time position in the U.S. is $180,000 to $270,000, supplemented by equity and additional benefits. Final compensation may vary outside this range, depending on factors such as role, level, and location. Individual pay will be determined based on job-related skills, experience, location, and relevant education or training.

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