

Strategic Staffing Solutions
Machine Learning Engineer
β - Featured Role | Apply direct with Data Freelance Hub
This role is for a Machine Learning Engineer in McLean, VA, for 2 years at $75-$90/hr W2. Requires a Bachelor's, 3+ years in application development, and expertise in big data technologies, cloud platforms, and real-time data applications.
π - Country
United States
π± - Currency
$ USD
-
π° - Day rate
720
-
ποΈ - Date
January 13, 2026
π - Duration
More than 6 months
-
ποΈ - Location
On-site
-
π - Contract
W2 Contractor
-
π - Security
Unknown
-
π - Location detailed
New York, NY
-
π§ - Skills detailed
#Azure #Batch #Kafka (Apache Kafka) #Microsoft Azure #SQL (Structured Query Language) #Scripting #Cloud #Snowflake #Shell Scripting #Python #Big Data #AWS (Amazon Web Services) #Java #Agile #MySQL #Scala #Spark (Apache Spark) #Hadoop #NoSQL #ML (Machine Learning) #Unix #Redshift #Public Cloud #Linux
Role description
Machine Learning Engineer - Lead
Location: McLean VA 22102 (Onsite)
Duration: 2 years
Pay- $75-$90/hr W2 ONLY, NO C2C
Overview:
β’ Recommender is a key driver of Incremental PV for Campaign Arbitration on EASE creating over $400MM PV generated via Machine Learning optimization. For 2026 the platform will expand to support Email arbitration gating 100M emails/month.
β’ The platform RTE is expected to increase by 75% through expanded batch data and model training infra.
β’ The additional NAL will critically support RTE for the email expansion as well as add bench strength for campaign RTE. This will free up FTEs for Q1/Q2 KRs to produce the Replay framework and take ownership of the model training infrastructure.
Basic Qualifications:
β’ Bachelorβs Degree
β’ At least 3 years of experience in application development (Internship experience does not apply)
β’ At least 1 year of experience in big data technologies
Preferred Qualifications:
β’ 5+ years of experience in application development including Python, SQL, Scala, or Java
β’ 2+ years of experience with a public cloud (AWS, Microsoft Azure, Google Cloud)
β’ 3+ years experience with Distributed data/computing tools (MapReduce, Hadoop, Hive, EMR, Kafka, Spark, Gurobi, or MySQL)
β’ 2+ year experience working on real-time data and streaming applications
β’ 2+ years of experience with NoSQL implementation (Mongo, Cassandra)
β’ 2+ years of data warehousing experience (Redshift or Snowflake)
β’ 3+ years of experience with UNIX/Linux including basic commands and shell scripting
β’ 2+ years of experience with Agile engineering practices
Machine Learning Engineer - Lead
Location: McLean VA 22102 (Onsite)
Duration: 2 years
Pay- $75-$90/hr W2 ONLY, NO C2C
Overview:
β’ Recommender is a key driver of Incremental PV for Campaign Arbitration on EASE creating over $400MM PV generated via Machine Learning optimization. For 2026 the platform will expand to support Email arbitration gating 100M emails/month.
β’ The platform RTE is expected to increase by 75% through expanded batch data and model training infra.
β’ The additional NAL will critically support RTE for the email expansion as well as add bench strength for campaign RTE. This will free up FTEs for Q1/Q2 KRs to produce the Replay framework and take ownership of the model training infrastructure.
Basic Qualifications:
β’ Bachelorβs Degree
β’ At least 3 years of experience in application development (Internship experience does not apply)
β’ At least 1 year of experience in big data technologies
Preferred Qualifications:
β’ 5+ years of experience in application development including Python, SQL, Scala, or Java
β’ 2+ years of experience with a public cloud (AWS, Microsoft Azure, Google Cloud)
β’ 3+ years experience with Distributed data/computing tools (MapReduce, Hadoop, Hive, EMR, Kafka, Spark, Gurobi, or MySQL)
β’ 2+ year experience working on real-time data and streaming applications
β’ 2+ years of experience with NoSQL implementation (Mongo, Cassandra)
β’ 2+ years of data warehousing experience (Redshift or Snowflake)
β’ 3+ years of experience with UNIX/Linux including basic commands and shell scripting
β’ 2+ years of experience with Agile engineering practices




