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Junior Machine Learning Engineer Jobs in Farmington, UT

OC Tanner

Sr. Applied AI Engineer

Salt Lake City, UT · On-site

$101K - $138K/yr

AWS Certified Machine Learning Engineer - Associate or equivalent * Cloud AI infrastructure management using AWS services and Terraform * AI observability experience with OpenTelemetry, Langfuse, or ...

Octanner

Sr. Applied AI Engineer

Salt Lake City, UT

$101K - $138K/yr

AWS Certified Machine Learning Engineer - Associate or equivalent * Cloud AI infrastructure management using AWS services and Terraform * AI observability experience with OpenTelemetry, Langfuse, or ...

Pwc

CTIO AI Engineering Manager

Salt Lake City, UT · On-site

$73K - $244K/yr

Those in data science and machine learning engineering at PwC will focus on leveraging advanced ... junior staff while upholding remarkable standards of quality and innovation in deliverables.

Pwc

AI Engineer

Salt Lake City, UT · On-site

$50K - $112K/yr

Certifications aligned to data engineering, machine learning, and cloud platforms, including AWS, Google Cloud, Microsoft Azure, Databricks, Snowflake, or related data and AI credentials - Building ...

University of Utah

Data Scientists

Salt Lake City, UT · On-site

$75K - $105K/yr

Apply knowledge of statistics, machine learning, programming, data modeling, simulation, and advanced mathematics to recognize patterns, identify opportunities, pose business questions, and make ...

Hatch

Junior Commissioning Engineer

Salt Lake City, UT · On-site

Hatch is seeking a Junior Commissioning Engineer with interest in metals, mining, energy, water ... Work in an exciting environment where you can apply your academic learning on a daily basis * Gain ...

Pwc

US Tech - AI Engineering Manager

Salt Lake City, UT · On-site

$73K - $244K/yr

Those in data science and machine learning engineering at PwC will focus on leveraging advanced ... Responsibilities - Mentor junior engineers and foster their growth - Maintain security and ...

PDCflow

Junior Software Engineer

Ogden, UT · Hybrid

Junior Software Engineer DAKCS is a 40-year industry veteran in accounts receivable management and ... If you like solving problems, learning constantly, and using the latest tech to do it, this role is ...

Pwc

NGA AI Engineer Manager

Salt Lake City, UT · On-site

$73K - $244K/yr

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

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Showing results 21-40

All JobsMachine Learning Engineer JobsJunior Machine Learning Engineer JobsJunior Machine Learning Engineer Jobs in Farmington, UT

Junior Machine Learning Engineer information

See Farmington, UT salary details

$31.8K

$68.1K

$103.9K

How much do junior machine learning engineer jobs pay per year?

As of Aug 16, 2026, the average yearly pay for junior machine learning engineer in Farmington, UT is $68,104.00, according to ZipRecruiter salary data. Most workers in this role earn between $46,000.00 and $75,900.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a junior machine learning engineer, and why are they important?

To succeed as a Junior Machine Learning Engineer, you need a solid grasp of programming (especially Python), foundational knowledge of algorithms and statistics, and a relevant degree in computer science, mathematics, or a related field. Familiarity with machine learning frameworks such as TensorFlow or PyTorch and tools like scikit-learn, as well as experience with version control systems like Git, are typically required. Strong problem-solving abilities, attention to detail, and a willingness to learn from feedback are valuable soft skills that help you adapt and grow in the field. These skills ensure you can effectively develop, test, and improve machine learning models while collaborating with more experienced engineers and contributing to team projects.

What kinds of projects and responsibilities can a junior machine learning engineer expect in their first year on the job?

As a Junior Machine Learning Engineer, you’ll typically work on tasks such as data preprocessing, building and testing simple models, and supporting more senior engineers in deploying machine learning solutions. Your responsibilities may also include cleaning datasets, implementing basic algorithms, and running experiments to evaluate model performance. You’ll often collaborate closely with data scientists, software engineers, and product teams to understand project goals and learn best practices. The role provides excellent opportunities to develop your technical skills, gain exposure to various stages of the ML pipeline, and gradually take on more complex projects as you grow.

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

AspectJunior Machine Learning EngineerData Scientist
Required CredentialsBachelor's in CS, Data Science, or related; some experience with ML frameworksBachelor's or higher in CS, Statistics, or related; often advanced certifications
Work EnvironmentDeveloping and deploying ML models, coding, testingData analysis, statistical modeling, interpreting data insights
Employer & Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, tech, consulting
Search & Comparison IntentYesYes

While both roles involve working with data and machine learning, Junior Machine Learning Engineers focus on building and deploying models, often with coding and engineering skills. Data Scientists analyze data, create statistical models, and interpret insights. The roles overlap but differ mainly in their core responsibilities and skill emphasis.

What does a junior machine learning engineer do?

As a junior machine learning engineer, you work in AI, performing research with algorithms and data modeling techniques. Machine learning involves using large collections of data to create systems that are capable of making predictions, and in this field, your duties and responsibilities revolve around using advanced mathematics to design applications for use in everything from stock trading to sports betting. Some machine learning efforts involve images, and this branch of the field is known as computer vision, while other techniques which focus on text are called natural language processing (NLP). Given these divisions, titles in machine learning include computer vision engineer, NLP scientist, or simply research scientist.

More about Junior Machine Learning Engineer jobs

What cities near Farmington, UT are hiring for Junior Machine Learning Engineer jobs?

Cities near Farmington, UT with the most Junior Machine Learning Engineer job openings:

Junior Machine Learning Engineer jobs near you
Infographic showing various Junior Machine Learning Engineer job openings in Farmington, UT as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $68,104 per year, or $32.7 per hour.

Sr. Applied AI Engineer

OC Tanner

Salt Lake City, UT • On-site

$101K - $138K/yr

Full-time

Re-posted 4 days ago

Job description

O.C. Tanner is the global leader in software and services that improve workplace culture through meaningful employee experiences. Our Culture Cloud is a suite of apps designed to enhance the employee experience with strategic recognition, service awards, wellbeing, leadership, and events that help people thrive at work. Our Culture by Design approach provides expert services to organizations looking to create great workplaces.
Our global team of 1,500 people hail from 58 countries and speak 62 languages. As programmers, researchers, designers, client professionals and craftspeople we create the tech, tools and awards that connect employees to purpose at thousands of companies. Join us as we help people all over the world thrive at work.
About the Role
AI is becoming part of the product and platform architecture we need to build, operate, and scale. We are looking for an Applied AI Engineer who can turn AI capability into secure, measurable, governed production systems, not prototypes or demos. This person will help define how O.C. Tanner builds agentic systems that pursue goals, use tools, follow guardrails, recover from failure, and deliver real value inside user workflows.
This role sits at the intersection of software engineering, product experience, AI platform engineering, and responsible AI. You will partner with Product, UX, Design, Architecture, Security, and Engineering to build AI experiences that are useful, understandable, reliable, and safe to operate in production. The right person has hands-on experience building agentic systems with orchestration, tool calling, memory or state, RAG, evaluation, observability, and human-in-the-loop controls.
Responsibilities
  • Design, build, deploy, and support production-grade agentic AI systems that operate against explicit goals, constraints, policies, and guardrails.
  • Build agent orchestration patterns for multi-step workflows, tool calling, MCP servers, state management, memory, retries, recovery paths, and human-in-the-loop controls.
  • Partner closely with Product, UX, Design, Architecture, Security, and Engineering teams to create AI experiences that are useful, understandable, reliable, and aligned with real user workflows.
  • Design user-centered AI interactions, including conversational flows, feedback loops, confidence handling, explainability, graceful failure modes, escalation paths, and clear boundaries for autonomous behavior.
  • Develop and operate RAG systems that ground model behavior in enterprise knowledge, including ingestion, chunking, embeddings, vector and hybrid retrieval, reranking, retrieval evaluation, and citation or traceability strategies.
  • Define and implement evaluation frameworks for AI systems, including offline test sets, regression suites, adversarial testing, groundedness and faithfulness scoring, task completion metrics, and production quality monitoring.
  • Instrument agentic systems for observability, including traces of model calls, prompts, tool usage, decisions, retrieved context, latency, cost, errors, and user feedback.
  • Establish safeguards for responsible AI use, including prompt injection defense, data access controls, PII protection, bias and toxicity detection, misuse prevention, audit logging, and policy enforcement.
  • Optimize model selection, prompts, context windows, caching, routing, inference patterns, latency, throughput, reliability, and cost across production workloads.
  • Mentor engineers on applied AI practices, including prompt and context engineering, agent design, RAG, evaluation, safety, observability, and production support.
  • Stay current with emerging AI platforms, frameworks, models, and standards.

Our stack
  • Python / FastAPI microservices
  • LangChain / LangGraph
  • GraphQL / REST
  • PostgreSQL / Redis
  • Kafka
  • Kubernetes
  • AWS Bedrock
  • OpenTelemetry
  • Terraform

Qualifications
Required Qualifications
  • 5+ years of software engineering experience with strong Python proficiency
  • 2+ years building production ML or agentic AI systems
  • 1+ years hands-on experience with agentic frameworks (LangGraph, CrewAI, AutoGen, or equivalent)
  • Built production AI systems including agents, MCP servers, multi-step reasoning, and multi-turn conversation
  • Deployed RAG systems including embedding models, vector databases, hybrid search, and retrieval optimization
  • Designed LLM strategies covering tool calling, structured outputs, prompt engineering, and context window management
  • Implemented AI safety and evaluation pipelines covering bias detection, PII leakage, faithfulness scoring, toxicity, and prompt injection mitigation
  • Optimized models for inference efficiency, latency, and cost management

Strongly Preferred
  • Bachelor's degree in Computer Science, Machine Learning, or a related field
  • AWS Certified Machine Learning Engineer - Associate or equivalent
  • Cloud AI infrastructure management using AWS services and Terraform
  • AI observability experience with OpenTelemetry, Langfuse, or equivalent

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