Location: Europe, Role Type: Fractional, Remote / Worldwide
  Remote: Yes
  Willing to relocate: Yes
  Technologies: Kafka, Flink, Spark, Cassandra, Scala, Rust / PyO3 / Tokio, Python, Zookeeper, Databricks, Delta Lake, BigQuery, Redshift, Hive, Kinesis, Airbyte, Airflow, Temporal, DBT, Aerospike, Snowflake, PrestoSQL, Trino, Clickhouse, SQL, Vector DBs, Golang, gRPC/protobuf, Terraform, CUDA
  Resume: https://drive.google.com/file/d/1lZuSpneLDVzzYoeaA8or-m5m2dfYAMB4/view
  Email: in the profile (please do mention "Via HN" in the subject for your email to land in the right place in my inbox)

Core Focus Areas:

● Cassandra (Data Modeling, Troubleshooting Performance And Operational Issues)

● Apache Iceberg (Scaling, Tuning, Self-Hosted Setup)

● Stream Processing At Scale: Kafka, Flink, Spark Streaming, Storm

● Custom-Crafted Contextualized Embeddings, Vector-Based Semantic Search, Deep Intent Recognition In Search Engine Queries

● Languages: Scala, Rust, Python, SQL (proficient), Golang (ramping up)

Educational Background: Computer Science.

Solid experience working remotely and working with teams that are distributed geographically. I typically work Pacific Time hours.

Ask: $385K base, pro-rated.

(Assuming you prefer Python.)

Portable Two-Way Translator, Handheld

Portable Handheld Translator

Handheld Two-Way Translator

Electronic Two-Way Portable Instant Voice Translator, 40 Languages, Handheld

Based on previous experience with centroid-based algos, the function I wrote does a first pass throwing all words from all product titles into one big bag, then computing a centroid (frequency histogram with low-frequency words removed). The second pass is to compute a cosine similarity score for each product title (its own frequency histogram against the centroid). Whichever product title is the most similar to the centroid wins.

That algo may have existed already in some academic paper somewhere, but I came up with it independently.

Comments URL: https://news.ycombinator.com/item?id=46084721

Points: 7

# Comments: 3

Well-rounded Scala/Rust/Python dev, well-versed in data engineering, with deep knowledge of the internals of distributed datastores. I have experience with data modeling for high throughput database activity and a strong understanding of which workloads and data access patterns are scalable and what datastore the data should reside in. I have a highly confident ability to lead a data / MLops project from start to finish. Hire me.

Core Skills:

● Cassandra (Data Modeling, Troubleshooting Performance And Operational Issues)

● Apache Iceberg (Scaling, Tuning, Self-Hosted Setup)

● Stream Processing At Scale: Kafka, Flink, Spark Streaming, Storm

● Custom-Crafted TF-IDF / Embeddings, Vector-Based Semantic Search, Deep Intent Recognition In Search Engine Queries

● Languages: Scala, Rust, Python, SQL (proficient), Golang (ramping up)

Other Skills: Airflow, Aerospike, DBT, Snowflake, Qdrant, Databricks/DeltaLake, BigQuery, Redshift, Hive, Kinesis, PrestoSQL/Trino, ClickHouse, gRPC, Airbyte, Terraform, CUDA, Reverse-Engineering Search Engine Technologies.

Educational Background: Computer Science.

Solid experience working remotely and working with teams that are distributed geographically. I typically work Pacific Time hours.

E-mail address in the profile.

Over a few years, I came to rely on proxy indicators that I found to be reliably in sync with the strength / weakness of the job market for software engineers.

One of these indicators is the business spending index (it has an official name, something about purchasing managers' sentiment index or similar). CNBC tends to show it a lot these days, you can't miss it.

Another one is "US Auto Loans Delinquent by 90 or More Days (I:USALD90)" which is a very good "finger to the wind" for how the overall economy is doing, since everyone needs a car.

Neither of these indicators are looking good these days.

A certain company (the name starts with "A") is widely known for doing this, you might guess which company I refer to by deep-diving into my comments history.

It looks exactly like in this YT short: https://www.youtube.com/shorts/V5VAN6ldS9o

  > prefer Craigslist

But they too will behave in the same way at some point, due to compliance requirements. Banks and fintech companies need to know 1. who you are 2. that your income is from legit sources.

For #2, ask your customers (who paid you recently) to give you a copy of the banking app records of those payments, where the last 4 digits of the payer's bank account can be seen. Wise can then cross-reference those against what they see on their end, at which point they can declare your source of income verified. (I had to learn this the hard way myself.) Very importantly, those payment records have to be from the banking app that the accounts payable team is using, not Ripple or any internal payroll system.

You would be doing the world a favor, the existing options for this are owned by corrupt / retrograde companies.

https://www.perplexity.ai/ (good at general knowledge)

https://www.phind.com/ (good at coding tasks and synthesizing knowledge from stackoverflow)

https://you.com/search?q=who+are+you&tbm=youchat&cfr=chat (failed at coding tasks on more than one occasion)

https://chatbot.theb.ai/ (this one seems to be Baidu's answer to ChatGPT)

Whenever you need to add a search experience to your SaaS project, you don't really think of building it yourself, however the established companies in the search engine space are lazy, retrograde and corrupt, and they absolutely don't deserve your money. (I know because I work for one of them.)

OpenAI has recently announced it will slash the cost of calls to its embeddings API by a whopping 75%. This is a huge opportunity for a startup to disrupt the search engine space.

With access to OpenAI's now-affordable embeddings API and to a vector database, one mid-level engineer can build a highly scalable custom search engine for ecommerce and retail, in a few weeks.

And yet nobody is talking about this.