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Contract Machine Learning Startup Jobs in California

Goodfire

Machine Learning Engineer

San Francisco, CA

$200K - $400K/yr

About the role We're looking for Machine Learning Engineers to help build our platform for training ... Startup or frontier lab experience in fast-moving teams. Our values Goodfire is looking for ...

Motorola

Machine Learning Engineer

Los Angeles, CA

$150K - $180K/yr

You will join the Signals and Systems group where you will be developing machine learning solutions ... required by government contract. Travel RequirementsNone Relocation ProvidedNone Position ...

DocuSign

Machine Learning Engineer

San Francisco, CA · On-site +1

$164K - $266K/yr

... contract lifecycle management (CLM). What you'll do As a Machine Learning Engineer on the AI ... Platform team, you will design and build the foundational infrastructure that powers Docusign ...

Docusign

Machine Learning Engineer

San Francisco, CA · On-site

$164K - $266K/yr

... contract lifecycle management (CLM). What you'll do As a Machine Learning Engineer on the AI ... Platform team, you will design and build the foundational infrastructure that powers Docusign ...

Goodfire

Machine Learning Engineer

San Francisco, CA · On-site

$200K - $400K/yr

About the role We're looking for Machine Learning Engineers to help build our platform for training ... Startup or frontier lab experience in fast-moving teams. Our values Goodfire is looking for ...

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Contract Machine Learning Startup information

What are some common challenges faced by machine learning professionals working on a contract basis at startups?

Machine learning professionals working as contractors at startups often face challenges such as rapidly changing project scopes, limited access to large datasets, and the need to quickly adapt to new tools and frameworks. Startups typically move fast, so contractors must be comfortable with ambiguity and prioritize delivering value in short timeframes. Additionally, they may need to collaborate closely with cross-functional teams, such as product managers and engineers, to ensure that machine learning solutions align with business goals.

What is the difference between Contract Machine Learning Startup vs Data Scientist?

AspectContract Machine Learning StartupData Scientist
CredentialsRelevant degrees, certifications in ML/AITypically similar credentials, often with advanced degrees
Work EnvironmentProject-based, startup setting, flexible hoursOffice or remote, corporate or research settings
Employer & IndustryStartups in tech, AI, or data-driven sectorsVaried industries including tech, finance, healthcare
Search & Comparison IntentUnderstanding contract roles in ML startupsExploring data science career options

Contract Machine Learning Startup roles focus on short-term, project-based work within startup environments, often requiring specialized skills in ML and AI. Data Scientists typically work in more established companies or research settings, with similar credentials but often in a full-time capacity. Both roles demand strong technical backgrounds, but contract roles offer flexibility and varied projects, while Data Scientists may have more stability and broader responsibilities.

What are the key skills and qualifications needed to thrive in a Contract Machine Learning Startup role, and why are they important?

Success in a Contract Machine Learning Startup role generally requires expertise in machine learning algorithms, data analysis, and a solid background in computer science or related fields. Familiarity with programming languages such as Python or R, experience with ML frameworks like TensorFlow or PyTorch, and knowledge of cloud platforms (e.g., AWS, GCP) are typically expected. Strong problem-solving, adaptability, and effective communication help professionals collaborate with clients and respond to rapidly changing project requirements. These skills and qualities are vital to deliver innovative, scalable solutions in fast-paced, outcome-driven startup environments.

What is a Contract Machine Learning Startup?

A Contract Machine Learning Startup is a company or team that provides machine learning solutions and services to clients on a contract basis. Instead of developing their own products, these startups typically work with other businesses to build custom machine learning models, analyze data, and help integrate AI technologies into existing workflows. They may offer expertise in areas such as natural language processing, computer vision, or predictive analytics, and usually operate on short-term or project-based contracts. This approach allows client companies to access specialized knowledge without hiring full-time data scientists or engineers.
What are the most commonly searched types of Machine Learning Startup jobs in California? The most popular types of Machine Learning Startup jobs in California are:
What are popular job titles related to Contract Machine Learning Startup jobs in California? For Contract Machine Learning Startup jobs in California, the most frequently searched job titles are:
What job categories do people searching Contract Machine Learning Startup jobs in California look for? The top searched job categories for Contract Machine Learning Startup jobs in California are:
What cities in California are hiring for Contract Machine Learning Startup jobs? Cities in California with the most Contract Machine Learning Startup job openings:
Contract Machine Learning Startup jobs near you
Software Engineering Lead, Machine Learning

Software Engineering Lead, Machine Learning

Ema

San Francisco, CA • On-site

$135K - $300K/yr

Full-time

Posted 21 days ago

Job description

About Ema
Ema is building the world's leading Agentic AI platform to transform enterprise productivity. We enable organizations to delegate repetitive tasks to Ema, the Universal AI Employee, delivering 10x gains in workforce efficiency, across functions. Founded by former executives from Google, Coinbase, Flipkart, and Okta, our team includes engineers from premier tech companies and graduates of Stanford, MIT, UC Berkeley, CMU, and IITs.
We are backed by industry leading investors including Accel, Naspers/Prosus, Section32, and angels like Sheryl Sandberg and Dustin Moskovitz. Headquartered in Silicon Valley and with offices in London, Bangalore and Vancouver and Bangalore, Ema is at the frontier of what Agentic AI can do in production - we ship real systems that run real business processes at scale.
Who you are
We're looking for an innovative and passionate Machine Learning Engineers to join our team. You are someone who loves solving complex problems, enjoys the challenges of working with huge data sets, and has a knack for turning theoretical concepts into practical, scalable solutions. You are a strong team player but also thrive in autonomous environments where your ideas can make a significant impact. You love utilizing machine learning techniques to push the boundaries of what is possible within the realm of Natural Language Processing, Information Retrieval and related Machine Learning technologies. Most importantly, you are excited to be part of a mission-oriented high-growth startup that can create a lasting impact.
You will:
  1. Conceptualize, develop, and deploy machine learning models that underpin our NLP, retrieval, ranking, reasoning, dialog and code-generation systems.
  2. Implement advanced machine learning algorithms, such as Transformer-based models, reinforcement learning, ensemble learning, and agent-based systems to continually improve the performance of our AI systems.
  3. Lead the processing and analysis of large, complex datasets (structured, semi-structured, and unstructured), and use your findings to inform the development of our models.
  4. Work across the complete lifecycle of ML model development, including problem definition, data exploration, feature engineering, model training, validation, and deployment.
  5. Implement A/B testing and other statistical methods to validate the effectiveness of models. Ensure the integrity and robustness of ML solutions by developing automated testing and validation processes.
  6. Clearly communicate the technical workings and benefits of ML models to both technical and non-technical stakeholders, facilitating understanding and adoption.

Ideally, you'd have:
  1. A Master's degree or Ph.D. in Computer Science, Machine Learning, or a related quantitative field.
  2. Proven industry experience in building and deploying production-level machine learning models.
  3. Deep understanding and practical experience with NLP techniques and frameworks, including training and inference of large language models.
  4. Deep understanding of any of retrieval, ranking, reinforcement learning, and agent-based systems and experience in how to build them for large systems.
  5. Proficiency in Python and experience with ML libraries such as TensorFlow or PyTorch.
  6. Excellent skills in data processing (SQL, ETL, data warehousing) and experience working with large-scale data systems.
  7. Experience with machine learning model lifecycle management tools, and an understanding of MLOps principles and best practices.
  8. Familiarity with cloud platforms like GCP or Azure.
  9. Familiarity with the latest industry and academic trends in machine learning and AI, and the ability to apply this knowledge to practical projects.
  10. Good understanding of software development principles, data structures, and algorithms.
  11. Excellent problem-solving skills, attention to detail, and a strong capacity for logical thinking.
  12. The ability to work collaboratively in an extremely fast-paced, startup environment.

For California based candidates:
The standard base salary for this position is $135,000-$300,000 annually.
Compensation offered will be determined by factors such as location, level, job-related knowledge, skills, and experience. Certain roles may be eligible for variable compensation, equity, and benefits.
Ema Unlimited is an equal opportunity employer and is committed to providing equal employment opportunities to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, sexual orientation, gender identity, or genetics.

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