Listing unavailableData & AnalyticsOn-site

Machine Learning Scientist

Tacit · On-site

This listing is no longer verified as available.

MeritLog keeps this source-backed description for reference. Availability is not verified, and there is no application link here.

Last seen by MeritLog September 8, 2026Source: AshbySource version: ashby-public-job-posting-v1

Source: the employer's Ashby job board. Open the original listing for current details. Availability is not verified for this retained page.

Job details

Work model: On-site
Salary: Not listed by source
Location: San Francisco
Occupation: Data Scientists(O*NET 15-2051.00)

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

Keep exploring

Available Data & Analytics roles

These current listings are available to explore now.

Search all jobs