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Machine Learning Engineer Jobs in Simi Valley, CA

Paramount

Machine Learning Engineer

Burbank, CA · On-site

$109K - $143K/yr

Overview We are seeking a Senior Lead / Lead ML Platform Engineer to architect and own the ... learning. * High-Performance Inference: Design and maintain K8s-based inference servers (e.g ...

Paramount

Machine Learning Engineer

Burbank, CA

$109K - $143K/yr

Overview We are seeking a Senior Lead / Lead ML Platform Engineer to architect and own the ... learning. * High-Performance Inference: Design and maintain K8s-based inference servers (e.g ...

Capgroup

Senior Machine Learning Engineer

Los Angeles, CA

$112K - $154K/yr

... Machine Learning Engineering team to build the next generation of AI products at Capital Group - including agentic systems, LLM-powered workflows, and the platform that ensures they are safe ...

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All JobsMachine Learning Engineer JobsMachine Learning Engineer Jobs in Simi Valley, CA

Machine Learning Engineer information

See Simi Valley, CA salary details

$32.5K

$132.9K

$199.8K

How much do machine learning engineer jobs pay per year?

As of Jul 17, 2026, the average yearly pay for machine learning engineer in Simi Valley, CA is $132,928.00, according to ZipRecruiter salary data. Most workers in this role earn between $104,800.00 and $160,000.00 per year, depending on experience, location, and employer.

What engineers make $500,000?

Senior machine learning engineers with extensive experience, advanced skills in deep learning and data science, and often working in high-demand industries or companies can earn $500,000 or more annually. Compensation typically includes base salary, bonuses, and stock options, especially in tech giants or startups with significant funding.

What do machine learning engineers do?

Machine learning engineers develop algorithms and models that enable computers to learn from data and make predictions or decisions. They often work with large datasets, use programming languages like Python or Java, and utilize tools such as TensorFlow or PyTorch to build, test, and deploy machine learning systems in production environments.

What are Machine Learning Engineers?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready solutions. Their responsibilities include data preprocessing, model selection, algorithm implementation, and optimizing models for performance and efficiency. Machine Learning Engineers often collaborate with data scientists, software developers, and other stakeholders to integrate AI technologies into products and services.

What are the key skills and qualifications needed to thrive as a Machine Learning Engineer, and why are they important?

To thrive as a Machine Learning Engineer, you need strong programming skills (particularly in Python), a solid background in mathematics and statistics, and a degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and cloud platforms is typically required. Problem-solving ability, effective communication, and adaptability are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies ensure the development, deployment, and continual improvement of machine learning systems that drive business value.

Which 5 jobs will survive AI?

Machine Learning Engineers are likely to continue to be in demand as AI advances, as they develop and refine algorithms, models, and systems. Roles that require complex problem-solving, creativity, and domain expertise—such as healthcare professionals, data scientists, software developers, cybersecurity specialists, and AI ethics officers—are also expected to persist due to their reliance on human judgment and specialized knowledge. These jobs often involve skills that are difficult for AI to fully replicate or replace.

What Does a Machine Learning Engineer Do?

A machine learning engineer maintains production systems and often works with other engineers. In this career, you work with software development methodology, use modern software development tools, and use agile practices. You also play a role in software design and architecture, so you may occasionally work with a programmer. An engineer may help to predict how a model should perform or seek out regression issues by using different test types and algorithms. To fulfill your duties and responsibilities, you work on a computer and use an array of skills and programs to carry out these tests.

What engineers make $300,000 a year?

Senior machine learning engineers and data scientists with extensive experience, advanced skills in deep learning, and proficiency with tools like TensorFlow or PyTorch can earn $300,000 or more annually, especially in high-cost-of-living areas or top tech companies. Compensation often includes base salary, bonuses, and stock options, reflecting their expertise and impact on business outcomes.

What are some common challenges faced by Machine Learning Engineers when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining data consistency between training and production environments, and monitoring model performance over time. Integrating models into existing software infrastructure may require collaboration with DevOps and software engineering teams to address issues like latency, version control, and resource allocation. Additionally, ongoing model maintenance is crucial to prevent model drift and ensure that predictions remain accurate as new data becomes available.

What is the difference between Machine Learning Engineer vs Data Scientist?

AspectMachine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops scalable ML models, deploys algorithms into productionAnalyzes data, builds models, interprets data insights
Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research organizations

While both roles work with data and machine learning, Machine Learning Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights. The roles often overlap but differ in their core responsibilities and focus areas.

More about Machine Learning Engineer jobs
What are the most commonly searched types of Machine Learning Engineer jobs in Simi Valley, CA? The most popular types of Machine Learning Engineer jobs in Simi Valley, CA are:
What are popular job titles related to Machine Learning Engineer jobs in Simi Valley, CA? For Machine Learning Engineer jobs in Simi Valley, CA, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer jobs in Simi Valley, CA look for? The top searched job categories for Machine Learning Engineer jobs in Simi Valley, CA are:
What cities near Simi Valley, CA are hiring for Machine Learning Engineer jobs? Cities near Simi Valley, CA with the most Machine Learning Engineer job openings:
Machine Learning Engineer jobs near you
Infographic showing various Machine Learning Engineer job openings in Simi Valley, CA as of July 2026, with employment types broken down into 94% Full Time, 3% Part Time, and 3% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution, with an average salary of $132,928 per year, or $63.9 per hour.
Machine Learning Engineer

Machine Learning Engineer

Paramount

Burbank, CA • On-site

$109K - $143K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 22 hours ago

Paramount Senior Living rating

5.3

Company rating: 5.3 out of 10

Based on 19 frontline employees who took The Breakroom Quiz

Job description

#WeAreParamount on a mission to unleash the power of content... you in?
We've got the brands, we've got the stars, we've got thepowerto achieve our mission to entertain the planet - now all we're missing is... YOU! Becoming a part of Paramount means joining a team of passionate people who not only recognize the power of content but also enjoy a touch of fun and uniqueness. Together, we co-create moments that matter - both for our audiences and our employees - and aim to leave a positive mark on culture.
Overview
We are seeking a Senior Lead / Lead ML Platform Engineer to architect and own the technical direction for our Training and Inference infrastructure. This is a high-leverage role designed for an expert who understands the deep technical stack required to shift ML models from research to global production. You will be responsible for the "engine room" of the AMLG, ensuring that our MLEs can train massive models efficiently and serve them with sub-millisecond reliability. This role requires a unique blend of expertise in distributed systems and hardware acceleration. You will lead the adoption and optimization of AnyScale (Ray) for distributed training and manage a high-performance Kubernetes-based inference environment. You aren't just managing clusters; you are building a seamless, scalable platform that abstracts the complexity of GPUs and distributed compute for the entire organization.
Why This Role Matters
The ML Platform Lead is the force-multiplier for every other ML pod. In this role, you will directly shape:
  • The Training Foundation: Establishing AnyScale/Ray as the standard for distributed compute, enabling MLEs to train models on petabytes of data without managing infrastructure.
  • Inference at Scale: Architecting the serving layer that handles billions of requests per day, optimizing for both p99 latency and GPU utilization.
  • Operational Excellence: Setting the organizational standards for how ML models are deployed, monitored, and scaled across the enterprise.

Key Responsibilities
  • Technical Roadmap & Strategy: Own the long-term architectural direction for the Training and Inference domains, ensuring the platform scales 10x over a 1-3 year horizon.
  • Distributed Training Leadership: Lead the implementation and optimization of Ray/AnyScale, providing a unified compute layer for batch processing, model training, and reinforcement learning.
  • High-Performance Inference: Design and maintain K8s-based inference servers (e.g., Triton, TorchServe, or vLLM) optimized for GPU memory management and high throughput.
  • Hardware & Cost Optimization: Navigate the trade-offs between different GPU instances (A100s, H100s, T4s), optimizing for cost, availability, and performance.
  • Cross-Team Standardization: Solve high-leverage problems that affect multiple pods (e.g., Entry, Session, Presentation), establishing reusable patterns for CI/CD, model versioning, and canary deployments.
  • Reliability Engineering: Define and enforce SLIs/SLOs for the platform, ensuring that infrastructure failures never interrupt the user-facing personalization experience.
  • Mentorship & Coaching: Act as a technical mentor to senior engineers across the ML Platform and Applied ML pods, raising the bar for system design and operational rigor.

Basic Qualifications
  • 6-8+ years of experience in ML Infrastructure, Platform Engineering, or high-scale Backend Engineering.
  • Orchestration & Serving: Extensive experience with Kubernetes (K8s) and serving frameworks for large-scale ML models.
  • Hardware Proficiency: Strong knowledge of GPU architecture, CUDA, and optimizing ML workloads for hardware acceleration.
  • Leadership (IC4/5): Proven track record of owning the technical direction for a major domain anddriving impact across multiple teams.

Preferred Qualifications
  • Experience with Infra-as-Code (Terraform/Pulumi) and building automated MLOps pipelines.
  • Distributed Systems Mastery: Deep expertise with Ray (AnyScale) or similar distributed compute frameworks.
  • Familiarity with ML observability tools (Prometheus, Grafana, Weights & Biases, or MLFlow).
  • Experience managing multi-cloud or hybrid-cloud ML environments.
  • Deep knowledge of Python and C++ for performance-critical systems.

What Success Looks Like
In your first 6-12 months, you will:
  • Unify the Compute Layer: Successfully transition the majority of AMLG training workloads to a governed AnyScale/Ray environment.
  • Optimize Inference ROI: Measurably improve GPU utilization and reduce inference costs through better auto-scaling and server optimization.
  • Establish Durable Standards: Author the "Gold Standard" for ML deployments that is adopted by at least three other pods in the organization.
  • Reduce Systemic Risk: Implement a self-healing infrastructure layer that significantly reduces manual intervention for cluster-related failures.

#LI-KA1
Paramount Streaming, a division within Paramount Global, is the home to the company's direct-to-consumer services spanning free and paid in the form of Pluto TV and Paramount+. Pluto TV is the global leader in free ad-supported TV, delivering more than 1,400 global channels and an extensive library of streaming content, including live and original channels. Paramount+, digital subscription video-on-demand and live streaming service, combines live sports, breaking news, and A Mountain of Entertainment™. Paramount+ features an expansive library of original series, hit shows and popular movies across every genre from world-renowned brands and production studios, including SHOWTIME®.
ADDITIONAL INFORMATION
Hiring Salary Range: $130,200.00 - 195,300.00.
The hiring salary range for this position applies to New York, California, Colorado, Washington state, and most other geographies. Starting pay for the successful applicant depends on a variety of job-related factors, including but not limited to geographic location, market demands, experience, training, and education. The benefits available for this position include medical, dental, vision, 401(k) plan, life insurance coverage, disability benefits, tuition assistance program and PTO or, if applicable, as otherwise dictated by the appropriate Collective Bargaining Agreement. This position is bonus eligible.
What We Offer:
  • Attractive compensation and comprehensive benefits packages. Check out our full list of benefits here: https://www.paramount.com/careers/benefits
  • Generous paid time off.
  • An exciting and fulfilling opportunity to be part of one of Paramount's most dynamic teams.
  • Opportunities for both on-site and virtual engagement events.
  • Unique opportunities to make meaningful connections and build a vibrant community, both inside and outside the workplace.
  • Explore life at Paramount: https://www.paramount.com/careers/life-at-paramount

Paramount is an equal opportunity employer (EOE) including disability/vet.
At Paramount, the spirit of inclusion feeds into everything that we do, on-screen and off. From the programming and movies we create to employee benefits/programs and social impact outreach initiatives, we believe that opportunity, access, resources and rewards should be available to and for the benefit of all. Paramount is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ethnicity, ancestry, religion, creed, sex, national origin, sexual orientation, age, citizenship status, marital status, disability, gender identity, gender expression, and Veteran status.
If you are a qualified individual with a disability or a disabled veteran, you may request a reasonable accommodation if you are unable or limited in your ability to use or access https://www.paramount.com/careers as a result of your disability. You can request reasonable accommodations by calling 212.846.5500 or by sending an email to paramountaccommodations@paramount.com. Only messages left for this purpose will be returned.

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