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Machine Learning Contract Jobs in Santa Clara, CA

Spark Tek Inc is seeking a highly skilled Machine Learning Engineer to design and build a low ... Device identifiers (PID, Serial Number, MAC, Hostname), Smart / Virtual accounts, Orders, contracts ...

About the Role This is a Senior Machine Learning Engineer role embedded within a growing AI and ... This is a contract (W2) engagement. Visa sponsorship is not available. Location Based in Palo Alto ...

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Machine Learning Contract information

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How much do machine learning contract jobs pay per hour?

As of Aug 31, 2026, the average hourly pay for machine learning contract in Santa Clara, CA is $26.80, according to ZipRecruiter salary data. Most workers in this role earn between $23.17 and $29.90 per hour, depending on experience, location, and employer.

What is a machine learning contract?

A Machine Learning Contract job is a temporary or project-based role where professionals develop and implement machine learning models for a company. Contractors may work on tasks such as data preprocessing, model training, evaluation, and deployment. These roles are often remote or short-term, allowing companies to hire expertise for specific projects without long-term commitments.

What are the typical responsibilities and workflow for a machine learning contract?

As a Machine Learning Contract professional, you’ll often be brought in to design, build, and deploy machine learning models tailored to a client’s specific challenges, ranging from data preprocessing and exploratory analysis to model selection and performance tuning. You may also be responsible for documenting your work, presenting results to stakeholders, and advising on best practices for model integration. Contract positions frequently involve collaborating remotely with cross-functional teams and meeting project milestones within set timelines. This role is ideal for those who enjoy variety, autonomy, and leveraging their expertise across different industries and datasets.

What are the key skills and qualifications needed to thrive in a machine learning contract, and why are they important?

To thrive as a Machine Learning Contract professional, you need a solid background in programming (Python, R), data analysis, and machine learning algorithms, usually supported by a relevant degree in computer science or a related field. Familiarity with ML frameworks such as TensorFlow, PyTorch, and Scikit-learn, as well as experience with cloud platforms like AWS or Azure, is typically required. Strong problem-solving abilities, time management, and effective communication are standout soft skills in contract-based roles. These competencies are crucial for efficiently delivering project-based solutions, collaborating with clients, and staying adaptable to varied organizational needs.

What are the most commonly searched types of Machine Learning jobs in Santa Clara, CA?

The most popular types of Machine Learning jobs in Santa Clara, CA are:

What are popular job titles related to Machine Learning Contract jobs in Santa Clara, CA?

For Machine Learning Contract jobs in Santa Clara, CA, the most frequently searched job titles are:

What job categories do people searching Machine Learning Contract jobs in Santa Clara, CA look for?

The top searched job categories for Machine Learning Contract jobs in Santa Clara, CA are:

What cities near Santa Clara, CA are hiring for Machine Learning Contract jobs?

Cities near Santa Clara, CA with the most Machine Learning Contract job openings:

Infographic showing various Machine Learning Contract job openings in Santa Clara, CA as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 25% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $55,748 per year, or $26.8 per hour.

Machine Learning Engineer

Spark Tek Inc

San Jose, CA • On-site

Full-time

Re-posted 10 days ago


Job description

Job Summary:
Spark Tek Inc is seeking a highly skilled Machine Learning Engineer to design and build a low-latency query understanding and intelligent routing system. The role involves working on data modeling, ML development, optimization, and local deployment, focusing on extracting intent and supporting evidence from user queries in real time.
Responsibilities:
• Design and implement a query understanding pipeline to extract intent, routing decisions, entities, application mapping, and historical evidence from user queries and conversations.
• Define and build the training data model and annotation schema for structured outputs (intent, routing, entities, applications, evidence).
• Lead data collection, synthesis, analysis, and cleaning to develop high-quality datasets for model training and evaluation.
• Develop and evaluate baseline and advanced non-LLM models for:
• Intent classification
• Query routing
• Entity extraction
• Application detection
• Evidence retrieval
• Build and maintain train, test, and evaluation pipelines with strong focus on:
• Accuracy and F1 score
• Confidence scoring and calibration
• Latency and throughput
• Optimize models to meet strict constraints:
• Sub-second inference latency
• CPU-only execution
• Compact model size (<500MB)
• Deploy models locally within the application codebase, ensuring seamless integration without reliance on hosted AI services.
• Design and implement a Level 4 MLOps framework, including:
• Monitoring and alerting
• Drift detection
• Retraining pipelines
• Data feedback loops
• Develop strategies to handle domain evolution, including:
• New agents / skills
• New entity types
• Updates to domain definitions
• Leverage historical queries and routing decisions to improve prediction accuracy and evidence generation.
• Collaborate with product, engineering, and domain teams to translate business workflows into scalable ML solutions.
• Deliver a working demo / prototype baseline, and iteratively mature it into a production-ready system.
Qualifications:
Required:
• Strong expertise in Machine Learning and Applied NLP, especially in: Text classification, Intent detection, Query routing, Entity extraction, Semantic similarity and retrieval
• Proven experience with non-LLM approaches, including: Encoder-based models, Embedding-based pipelines, Classical ML (e.g., XGBoost, Logistic Regression), Lightweight deep learning models
• Experience designing training datasets, labeling frameworks, and structured output schemas for multi-task NLP systems
• Strong understanding of data preprocessing and quality improvement, including: Normalization, Deduplication, Class imbalance handling, Synthetic data generation
• Experience building robust evaluation frameworks, including: Precision, Recall, F1, Confidence scoring, Ranking quality, Latency measurement
• Hands-on experience with entity extraction for structured enterprise domains, such as: Device identifiers (PID, Serial Number, MAC, Hostname), Smart / Virtual accounts, Orders, contracts, subscriptions, Product families and licenses
• Experience handling multi-label and hierarchical classification problems
• Strong ability to build low-latency, CPU-optimized inference systems with strict memory and performance constraints
• Experience deploying ML models locally or on-prem within application codebases (not limited to cloud-hosted inference)
• Solid understanding of MLOps practices, including: Monitoring and observability, Drift detection, Retraining pipelines, Model lifecycle management
• Strong programming skills in Python, with hands-on experience in ML/NLP frameworks and pipeline orchestration
• Ability to adapt systems to continuous domain changes, including new skills, applications, and entities
Preferred:
• Prior experience in enterprise support systems, operational routing, licensing platforms, or device/account management domains is highly preferred
Company:
Spark Tek Inc offers IT solutions and consultancy services, specializing in IT services and consulting for various industries. Founded in 2021, the company is headquartered in Farmers Branch, USA, with a team of 51-200 employees. The company is currently Growth Stage.