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Machine Learning Jobs in California, MO (NOW HIRING)

Jobtailor

Principal Applied Scientist

California, MO · On-site

$180 - $280/hr

Defines and drives enterprise artificial intelligence and machine learning strategy aligned with organizational priorities * Leads the design, validation, deployment, monitoring, and continuous ...

Jobtailor

Software Engineer, ML Platform

California, MO · On-site

$140 - $200/hr

Experience designing and developing machine learning lifecycle infrastructure and platform services * Experience with feature stores, model development, deployment, and observability tools and ...

Jobtailor

Bioinformatics Scientist

California, MO · On-site

$120 - $180/hr

Develop and refine machine learning models for cell-free circulating tumor DNA fraction estimation and longitudinal treatment response monitoring * Improve molecular barcoding filtering strategies to ...

Jobtailor

Founding Data Scientist

California, MO · On-site

$120 - $180/hr

Core Competencies Demonstrates expertise in data science and machine learning, with a strong focus on building and optimizing risk and prediction models. Proficient in Python and data infrastructure ...

Jobtailor

Data Science Intern

California, MO · On-site

At least 6 months of experience or academic work using machine learning techniques * At least 6 months of experience with either Python, R or SQL Core Competencies Demonstrates expertise in data ...

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Showing results 41-60

All JobsMachine Learning JobsMachine Learning Jobs in California, MO

Machine Learning information

See California, MO salary details

$22.8K

$38.1K

$78.8K

How much do machine learning jobs pay per year?

As of Aug 22, 2026, the average yearly pay for machine learning in California, MO is $38,110.00, according to ZipRecruiter salary data. Most workers in this role earn between $29,100.00 and $41,200.00 per year, depending on experience, location, and employer.

What is a machine learning?

A Machine Learning job involves developing algorithms and models that enable computers to learn from data and make predictions or decisions without explicit programming. Professionals in this field work with large datasets, design and train machine learning models, and optimize them for performance and accuracy. Roles often require knowledge of programming languages like Python or R, experience with frameworks like TensorFlow or PyTorch, and an understanding of statistics and data science principles. Machine learning engineers and data scientists collaborate with software developers and domain experts to build AI-driven solutions for various industries.

What are the typical day-to-day responsibilities in a machine learning role?

As a machine learning professional, your daily tasks may include data preprocessing, developing and training models, evaluating performance metrics, and experimenting with algorithms to optimize results. You’ll often collaborate closely with data scientists, software engineers, and business stakeholders to align technical solutions with organizational goals. Regular activities can also involve deploying models to production, monitoring performance, and troubleshooting any issues that arise post-deployment. Staying up to date with recent ML research and participating in team discussions or code reviews are also common parts of the job.

What are the key skills and qualifications needed to thrive in a machine learning position?

To thrive in Machine Learning, you need a solid background in mathematics, statistics, programming (especially Python or R), and a formal degree in computer science, data science, or a related field. Experience with popular ML frameworks (such as TensorFlow, PyTorch, or Scikit-learn), version control, and relevant certifications like AWS Certified Machine Learning are highly valued. Strong problem-solving skills, curiosity, clear communication, and the ability to work both independently and within multidisciplinary teams make candidates stand out. These skills and qualities are essential for developing robust models, staying updated with technology advancements, and collaborating effectively on complex projects.

Is machine learning a high paying job?

Machine learning engineers and specialists are generally among the higher-paid roles in the tech industry due to their advanced skills in algorithms, programming, and data analysis. Salaries vary based on experience, location, and industry, but the field is known for competitive compensation compared to many other tech roles.

What jobs can I get with machine learning?

With a background in machine learning, you can pursue roles such as machine learning engineer, data scientist, AI researcher, or data analyst. These positions typically require skills in programming languages like Python or R, knowledge of algorithms, and experience with tools like TensorFlow or PyTorch.

What cities near California, MO are hiring for Machine Learning jobs?

Cities near California, MO with the most Machine Learning job openings:

Machine Learning jobs near you
Infographic showing various Machine Learning job openings in California, MO as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 22% Part Time, and 1% Contract. Highlights an 89% Physical, 2% Hybrid, and 9% Remote job distribution, with an average salary of $38,110 per year, or $18.3 per hour.

Machine Learning Engineer, Infra, AI for Drug Discovery

Jobtailor

California, MO • On-site

$140 - $190/hr

Other

Posted 6 days ago

Job description

  • Design, implement, ship, and operate scalable model-serving infrastructure for machine learning, scientific, LLM, and agentic workloads.
  • Help evolve our internal model deployment platform into a reliable, self-service platform for teams across the organization.
  • Improve platform scalability and reliability, including scale-to-zero, faster model startup, workload isolation, traffic management, and reduction of request failures and latency bottlenecks.
  • Build observability and operational tooling for model usage, latency, reliability, resource consumption, inference cost, bottlenecks, and service-level indicators.
  • Improve the usability of model deployment by developing validated configuration interfaces, reusable deployment patterns, APIs, command-line tools, and documentation.
  • Help converge real-time and batch inference workflows onto shared platform capabilities where appropriate.
  • Contribute to model lifecycle management infrastructure, including model registration and versioning, evaluation, promotion and release gates, monitoring, environment progression, and rollback.
  • Build event-driven integrations that connect model publication, evaluation, promotion, deployment, and retraining workflows.
  • Build consistent metrics and evaluation signals for understanding model cost, quality, reliability, and fitness for downstream workflows.
  • Partner with machine learning, data, scientific, and platform teams to translate requirements into maintainable solutions and remove infrastructure bottlenecks.
  • Own workstreams from design through implementation and production support, using strong software-engineering practices including testing, reviews, documentation, and incremental delivery.
Requirements
  • BS or MS in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
  • 3+ years of relevant industry experience in software engineering, infrastructure engineering, platform engineering, DevOps, MLOps, or a related area.
  • Strong Python programming skills and experience building and shipping maintainable production software, services, automation, or developer tooling.
  • Experience designing, deploying, or operating cloud systems (preferably on AWS) using services such as EKS, EC2, S3, IAM, SQS, SNS, and CloudWatch.
  • Experience with containers, Kubernetes, Helm, and IaC tools such as Terraform or Pulumi.
  • Experience with CI/CD, Git-based development workflows, automated testing, and software release practices.
  • Ability to troubleshoot complex systems using metrics, logs, traces, events, and observability tools such as Datadog, Prometheus, Grafana, or OpenTelemetry.
  • Understanding of distributed-systems concepts such as concurrency, queuing, retries, timeouts, idempotency, backpressure, and failure recovery.
  • Ability to gather requirements, communicate technical tradeoffs, and document systems for users and engineers with varied infrastructure experience.
  • Demonstrated ability to independently deliver practical, incremental solutions while considering immediate needs and longer-term platform direction.
  • Familiarity with model-serving or workflow-orchestration frameworks such as KServe, Triton, vLLM, Ray Serve, Prefect, or Dagster.
  • Experience optimizing model startup time, request throughput, batching, autoscaling, or GPU utilization.
  • Familiarity with model registries, experiment tracking, model evaluation, promotion workflows, or MLOps platforms.
  • Experience building event-driven systems using queues, event buses, or workflow orchestrators.
  • Familiarity with online and offline model evaluation, model-quality monitoring, data drift, or regression analysis.
  • Experience supporting scientific computing, high-performance computing, distributed training, or large-scale data processing.
  • Strong interest in the life sciences and drug discovery.
Core Competencies

Demonstrates expertise in building and operating scalable model-serving infrastructure, with strong proficiency in Python programming and cloud systems, particularly on AWS. Capable of improving platform reliability and usability through effective model lifecycle management and observability practices.

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