Forward Deployed Engineer

Morpheus Talent Solutions; San Francisco (CA); On-site; USD 200,000 - 300,000; Full time

Benefits offered by this job

401(k)

Daily meals and snacks

Transportation support for commuting

Job summary

Morpheus Talent Solutions is seeking a Forward Deployed Machine Learning Engineer in San Francisco to build production systems for large-scale multimodal data, collaborating closely with customers and partners.

You will own problems end-to-end, turning ambiguous requirements into reliable workflows, developing pipelines for collecting, transforming, filtering, labeling, and packaging datasets, and applying Generative AI and Physical AI in real-world projects.

Qualifications

• Experience working directly with customers, partners, or external technical teams.
• Strong Python skills and practical experience with PyTorch or a comparable ML framework.
• Hands-on experience with Generative AI, Physical AI, or related machine learning systems.
• Experience building ML systems, data pipelines, or production workflows.
• Strong intuition for dataset quality, filtering, labeling, evaluation, and difficult edge cases.
• Ability to turn ambiguous objectives into concrete technical plans and working systems.
• Ability to write clean, maintainable code while operating at startup speed.
• A strong interest in multimodal AI, video, media technologies, robotics, or intelligent physical systems.
• A bias toward shipping complete solutions rather than stopping at research prototypes.
• Comfort working across application code, machine learning, infrastructure, and data.

Responsibilities

• Work directly with technical customers and partners to understand complex and often ambiguous requirements.
• Design and build production systems for large-scale multimodal data.
• Develop pipelines and workflows for collecting, transforming, filtering, labeling, evaluating, and packaging datasets.
• Build custom algorithms, heuristics, and ML-powered systems when existing tools are insufficient.
• Work with data and models supporting Generative AI, Physical AI, computer vision, robotics, and other emerging AI applications.
• Translate high-level goals into concrete models, infrastructure, quality controls, and evaluation processes.
• Investigate edge cases and develop practical approaches to measuring and improving data quality.
• Collaborate closely with engineering, ML, research, and product teams.
• Own projects end-to-end, from initial problem definition through deployment and iteration.
• Move quickly while maintaining a high standard for reliability and maintainability.

Skills

Python

PyTorch

Generative AI

Physical AI

Data pipelines

Production workflows

Customer collaboration

End-to-end ownership

Multimodal data

ML systems

Job description

Compensation: $200,000 - $300,000 + Equity + Bonus

About the Company

We are working with an early-stage AI company building the data infrastructure and technology that enables the next generation of intelligent systems.

Their work spans large-scale video, image, audio, text, 3D, and other multimodal data. They work closely with sophisticated AI teams to solve difficult data and machine learning problems that cannot be addressed with off-the-shelf tooling.

The company is small, highly technical, and growing quickly. Engineers have significant ownership and are expected to take projects from ambiguous initial requirements through production deployment.

The Opportunity

We are looking for a Forward Deployed Machine Learning Engineer who enjoys operating at the intersection of engineering, machine learning, data, and real-world customer problems.

• You will work directly with technical teams to understand challenging data requirements, determine what needs to be built, and deliver working systems quickly.
• This role is particularly suited to someone excited about Generative AI and Physical AI, and interested in the data, models, and infrastructure required to advance these systems.
• You will turn ambiguous requirements into reliable production workflows that can discover, generate, process, filter, evaluate, and package large-scale multimodal datasets.
• This is not a role focused solely on model training or research. You will own problems end-to-end and see your work move rapidly from idea to production.

What You'll Do

• Work directly with technical customers and partners to understand complex and often ambiguous requirements.
• Design and build production systems for large-scale multimodal data.
• Develop pipelines and workflows for collecting, transforming, filtering, labeling, evaluating, and packaging datasets.
• Build custom algorithms, heuristics, and ML-powered systems when existing tools are insufficient.
• Work with data and models supporting Generative AI, Physical AI, computer vision, robotics, and other emerging AI applications.
• Translate high-level goals into concrete models, infrastructure, quality controls, and evaluation processes.
• Investigate edge cases and develop practical approaches to measuring and improving data quality.
• Collaborate closely with engineering, ML, research, and product teams.
• Own projects end-to-end, from initial problem definition through deployment and iteration.
• Move quickly while maintaining a high standard for reliability and maintainability.

What We're Looking For

• Experience working directly with customers, partners, or external technical teams.
• Strong Python skills and practical experience with PyTorch or a comparable ML framework.
• Hands-on experience with Generative AI, Physical AI, or related machine learning systems.
• Experience building ML systems, custom algorithms, data pipelines, or production workflows.
• Strong intuition for dataset quality, filtering, labeling, evaluation, and difficult edge cases.
• Ability to turn ambiguous objectives into concrete technical plans and working systems.
• Ability to write clean, maintainable code while operating at startup speed.
• A strong interest in multimodal AI, video, media technologies, robotics, or intelligent physical systems.
• A bias toward shipping complete solutions rather than stopping at research prototypes.
• Comfort working across application code, machine learning, infrastructure, and data.

Nice to Have

• Experience building or deploying systems for Generative AI or Physical AI applications.
• Familiarity with computer vision, robotics, simulation, embodied AI, or multimodal foundation models.
• Experience contributing to open-source software.
• Experience working at an early-stage startup or as an early engineering hire.
• Familiarity with data-centric approaches to machine learning.
• 401(k)
• Daily meals and snacks
• Transportation support for commuting home
• Opportunity to work on challenging problems at the intersection of data and frontier AI
• Significant ownership and autonomy from an early stage