Machine Learning Scientist
Tacit · On-site
MeritLog read this listing from Tacit's Ashby job board and last checked it on September 10, 2026.
Source: the employer's Ashby job board. Open the original listing for current details.
Job details
- Work model
- On-site
- Salary
- $180K - $270K
- Location
- San Francisco
- Occupation
- Data Scientists(O*NET 15-2051.00)
- Company website
- docs.google.com
Hiring context
How this role compares at Tacit
Tacit has 13 live roles in MeritLog’s catalog across 5 job families, and 3 of them are in data & analytics. 13 of those listings publish a pay range, a disclosure rate of 100%.
This role's posted range of $180K - $270K sits above 100% of the 9 other Tacit roles quoted over the same currency and period.
Counted across the job boards MeritLog tracks, at the time this page was served. Pay comparisons use only listings that publish a complete range in the same currency and period.
What the role asks for
What you'd do
- Design and implement state-of-the-art machine learning algorithms for processing multimodal biosignals, including time series, spatial, and spectral data.
- Build and optimize neural network architectures.
- Develop and evaluate multimodal learning techniques to fuse information from multiple sensor modalities.
- Iterate rapidly on model prototypes for real-time inference on custom hardware.
- Create and maintain a robust evaluation framework for benchmarking model performance across datasets and participants.
- Collaborate closely with a diverse team, including hardware engineers, neuroscientists, and product, to align models with user needs.
What they're asking for
- PhD in computer science, machine learning, computational neuroscience, or related fields (or equivalent industry experience).Education
- Expertise in deep learning frameworks (e.g., PyTorch, TensorFlow) and fluency in Python.Skill
- Track record of publishing or deploying machine learning models in real-world systems.Skill
- Independent work ethic, flexibility, and resourcefulness.Skill
- Effective communication and collaboration skills.Skill
- Comfortable in fast moving startup environment, excited to build independentlySkill
- Familiarity with human-machine interaction systems such as automatic speech recognition or neural interfaces.SkillPreferred
- Hands-on experience with consumer wearables or custom hardware.SkillPreferred
- Knowledge of low-latency inference techniques and model optimization for edge devices.SkillPreferred
- This position is full time, onsite in San Francisco (SOMA)SkillPreferred
- Company size: 30-40 peopleSkillPreferred
Parsed by MeritLog from the employer’s own posting. The full description follows below.
Job description
About Tacit We are an early-stage, deep tech startup based in San Francisco, developing innovative hardware that rethinks human-computer interaction. We are backed by General Catalyst, Khosla Ventures, and Greylock Partners, with a founding team from Stanford, BrainGate, Oculus, and Tesla. While we can’t reveal too much just yet, our team is tackling cutting-edge engineering challenges to bring revolutionary products to life. About the role As a Machine Learning Scientist, you will develop cutting-edge AI models to integrate and decode complex, multimodal data streams from our custom sensing hardware. You’ll play a pivotal role in advancing our technology stack by building and optimizing models for real-time applications. This position spans foundational research in deep learning, hands-on model development, and applying algorithms to scale across diverse data sources and users. Responsibilities: - Design and implement state-of-the-art machine learning algorithms for processing multimodal biosignals, including time series, spatial, and spectral data. - Build and optimize neural network architectures. - Develop and evaluate multimodal learning techniques to fuse information from multiple sensor modalities. - Iterate rapidly on model prototypes for real-time inference on custom hardware. - Create and maintain a robust evaluation framework for benchmarking model performance across datasets and participants. - Collaborate closely with a diverse team, including hardware engineers, neuroscientists, and product, to align models with user needs. Requirements: - PhD in computer science, machine learning, computational neuroscience, or related fields (or equivalent industry experience). - Expertise in deep learning frameworks (e.g., PyTorch, TensorFlow) and fluency in Python. - Track record of publishing or deploying machine learning models in real-world systems. - Independent work ethic, flexibility, and resourcefulness. - Effective communication and collaboration skills. - Comfortable in fast moving startup environment, excited to build independently Preferred Qualifications: - Familiarity with human-machine interaction systems such as automatic speech recognition or neural interfaces. - Hands-on experience with consumer wearables or custom hardware. - Knowledge of low-latency inference techniques and model optimization for edge devices. Details: - This position is full time, onsite in San Francisco (SOMA) - Company size: 30-40 people Compensation Range $180,000 - $270,000/year BENEFITS - Competitive equity package - Comprehensive medical, dental, and vision insurance - Unlimited PTO - Visa sponsorship - 4% 401k matching